Just a quick note to say that my 2021 WNBA selections will be posted at www.ats.io – the season begins May 14 and we’ll look to make it five straight winning seasons. We finished last year with a 21-16 record (56.75%). That’s our worst season over the past three years, but had Seattle to win the title at the beginning of the year, so it was a profitable season for us.

## My College Basketball Method

I know people are anxious for college hoops picks, but I need a few weeks worth of games to be in the books. I solely use stats from the current year and need each team to have several games under their belts. That should be around Dec. 14 but who knows with COVID?

Here is the college basketball betting chapter from the second edition of Becoming a Winning Gambler. It explains my method in detail and will get you going if you want to start a bit earlier.

College basketball has been fairly good to me over the past couple of years and my handicapping method is one that I presented in the first edition, so we’ll concentrate on it in this chapter and using it yielded a 154-123-6 (55.6%) record against the spread before the 201920 season came to an abrupt halt in March during the conference tournaments.

There were quite a few plays, but it’s important to note that in the daily college basketball write-ups I did to get that record, typically involved just the late games on the schedule. Due to time constraints of getting the articles out in a timely manner, I might have just looked at 12 games on a 40-game schedule. Somebody with more time could have easily had many more plays with a similar winning percentage, so once again, it comes down to time and how much you can put into your handicapping.

This method is a bit time consuming, as I mentioned earlier, but it does allow you to predict both sides and totals and is a valuable tool during the season. For this method you need the offensive and defensive averages for both teams playing, as well as both team’s average opponent power rating (AOPR), which is also referred to as strength of schedule. You also need the average number of points scored by college basketball teams, both for the full game, as well as in the first half.

### Obtaining the Numbers

To get the average number of points scored by college basketball teams, I’d again use SDQL, as it will just take several seconds.

Getting an AOPR figure is a little bit trickier. There are several websites that will have them posted, with USA Today’s Jeff Sagarin probably the most widely known, but I prefer to use Ken Pomeroy’s website at kenpom.com, but you do have to make an adjustment to his numbers.

According to Pomeroy, the Michigan Wolverines had the toughest schedule in college basketball for the 2019-20 season, a shade more difficult than the schedule of Kansas, who was listed as having the toughest schedule for the majority of the season.

Michigan’s strength of schedule rating for the season was a +12.79. To get an AOPR that can be used with this method, subtract Pomeroy’s highest strength of schedule rating from 100. In this case, the number is 87.21, as 100 – Michigan’s rating of 12.79 is 87.21. Add 87.21 to Pomeroy’s strength of schedule rating for every team to get your numbers.

For scoring averages you can use SDQL, but I found it’s easiest to use the game matchups found at StatFox, which not only gives you overall scoring averages, but also home or away averages depending on the location of the game, as well as first-half scoring averages, which makes doing our first-half calculations much quicker.

### Using the System

Once you have the average number of all teams, you will compare it to the numbers of both teams playing in the game that you are handicapping.

For an example, we will use an average number of 68.1 and look at a game between Team A and Team B. Team A is the road team and is averaging 70.1 points per game and allowing 64.6 points a game with an AOPR of 71.7. Team B is averaging 68.8 points per game and allowing 69.4 points per game with an AOPR of 76.4.

The first step is to compare both teams’ averages against the average and come up with offensive and defensive percentages. Team A is scoring 70.1 points per game, which divided by 68.1 gives an offensive percentage of 1.03, while Team B is scoring 68.8 points per game, which divided by 68.1 gives an offensive rating of 1.01.

For defense, Team A is allowing 64.6 points per game, which divided by 68.1 yields a defensive rating of .95. Team B is allowing 69.4 points per game, which divided by 68.1 yields a defensive rating of 1.02.

The next step is to add Team A’s offensive rating to Team B’s defensive rating and then subtract one, giving a figure of 1.05. Do the same for Team B, using its offensive rating of 1.01 and Team A’s defensive rating of .95 to get a figure of 1.96, which becomes .96 after you subtract one.

Next, multiply Team A’s figure of 1.05 by the median figure of 68.1 to get a predicted score of 71.5 points. Multiplying Team B’s figure of .96 by 68.1 yields a predicted score of 65.4 points.

The next step is to factor in AOPR and you will divide the higher AOPR by the lowest to get a percentage. Team B’s AOPR of 76.4 divided by Team A’s AOPR of 71.7 gives a figure of 1.07, meaning Team B has played a schedule that is 7% more difficult than Team A.

Next, divide the percentage in two, which will give you 3.5%, meaning you will decrease the score of the team with the lower AOPR by 3.5% and increase the score of the team with the higher AOPR by 3.5%. The new projections would now be for Team A to score 69.1 points, which is 71.5 divided by 1.035, and Team B to score 67.7 points, which is 65.4 multiplied by 1.035.

The final step is to factor in four points for home court advantage. Since you are using the numbers to predict a final score you can’t simply add four points to the home team, as that will throw off your predicted total by four points. Instead subtract two points from the visitor’s score and add two points to the home team’s score, which in this case will give a predicted score of Team B 69.7, Team A 67.1.

Let’s look at one more example, using the same average of 68.1, between Team C and Team D. Team C is the road team and is averaging 75.8 points and allowing 79.6 with an AOPR of 77.2. Team D is averaging 72 points a game, allowing 68.7 and has an AOPR of 73.6.

Team C’s offensive rating is calculated by dividing 75.8 by 68.1 to get a figure of 1.11, while Team C’s defensive rating is calculated by dividing 79.6 by 68.1 to get a figure of 1.17. For Team D, its offensive rating is figured by dividing 72 by 68.1 to get a number of 1.06, while Team D’s defensive rating is 68.7 divided by 68.1 to get 1.01.

Team C’s offensive rating of 1.11 plus Team D’s defensive rating of 1.01 equals 2.12. After subtracting one you get a figure of 1.12. Team D’s offensive rating of 1.06 plus Team C’s defensive rating of 1.17 equals 2.23, which becomes 1.23 after one is subtracted.

Team C’s rating of 1.12 is multiplied by the median of 68.1 to get a predicted score of 76.3 points, while Team D’s rating of 1.23 is multiplied by the median to get a figure of 83.8 points.

Team C has the higher AOPR of 77.2, which divided by Team D’s AOPR of 73.6, is 1.05, meaning Team C’s schedule has been 5% more difficult than that played by Team D. Dividing 1.05 in half gives 1.025, meaning that Team C will see an increase of 2.5% in its predicted score, while Team D receives a decrease of 2.5% for its predicted score.

Team C’s predicted score of 76.3 multiplied by 1.025 becomes 78.2, while Team D’s predicted score of 83.8 points divided by 1.025 becomes 81.8. When you subtract two points from Team C and add two points to Team D for home court advantage, the final predicted score becomes Team D 83.8-76.2.

This method is loosely based on one presented in Sports Betting: A Winner’s Handbook by Jerry L. Patterson and Jack Painter and can be used during the course of the season as a solid mathematical foundation for your handicapping.

Despite my limited Excel abilities, I was able to create a spreadsheet that I use to do the calculations, so it’s just a matter of plugging in the numbers.

### First-half handicapping

I’ve had a fair amount of success the past few years betting first halves and the calculation method is exactly the same. The only difference is in the numbers we use, which are first half scoring figures for both the teams and the league-wide scoring average.

One thing I like about betting first halves is that sportsbooks tend to follow a formula for creating first-half lines and totals. The spread is typically half of the full-game line, but that will climb for larger favorites. A team that is favored by 4 points for the game will be favored by 2 points in the first half. But an 18-point favorite for the game may be favored by 10.5 points in the first half.

For first-half totals, the ballpark figure is half of the full game total and then subtract 4.5. A contest with a full-game total of 140, will see a first half total of roughly 65.5, while a game with a total of 150 will see close to a 70.5 for the first half.

Some teams play better in the first half than they do in the second half, especially certain weaker teams, who may not be very deep and are forced to rely on their starters too much. Fatigue starts to set in during the second half of the game and things begin to come unraveled. But these teams can be decent wagers in the first half and some poor teams had solid scoring averages for the first half.

For first half wagers, I liked to have a difference of at least three points between the projection and the line. I also look at the home and away tendencies of both teams and ideally, they will agree with the play. If the numbers like the under, the road team will see fewer points away from home than their overall average, while the home team will have lower-scoring first halves in front of the home fans.

If the overall numbers like one side, but a team tends to play the opposite in their location, I will frequently pass the game. If the numbers are calling for 62 points in the first half of a game between Iona at Marist, but Marist sees 6.4 more points in the first half of their home games compared to their overall numbers, I would most likely sit the game out.

## College Football Market Report 11-14-20

We won both plays at the ATS site on Friday, but still sitting at an unsightly 13-14 with two plays left for Saturday. Now, we’ll take a look at the remaining games for Saturday and look at the betting patterns that are taking place.

College hoops is getting ready to begin soon and we’ll be on top of it pretty good.

The college football betting markets are taking a few stands today.

**Vanderbilt at Kentucky:** Kentucky has moved from -17.5 to -18 even though the Wildcats have received less than 50% of the wagers. The total is just 41.5. Favorites of 17 or more are just 37-51-2 ATS when the total is less than 42.

**Notre Dame at Boston College:** Notre Dame opened -13 and the line is down to 11.5 on even betting. Tough spot for the Irish but everybody knows that and no real value on the Eagles. I’d lean BC if I had to play.

**Wisconsin at Michigan:** Wisconsin getting hammered right now and the line is up to 6.5. I’m on the other side in this one.

**Northwestern at Purdue:** Northwestern getting hit hard, as the Wildcats are now favored by 3. The game opened even and the betting has been split.

**Temple at Central Florida:** The Knights have moved from -28.5 to -25.5 after getting 67% of the bets.

Late leans (based on game-day betting patterns): North Carolina -12.5, Illinois +6, Ole Miss -13, Fresno -10.

## NFL Betting Methods – Week 10

In the update to Being A Winning Gambler – Second Edition, I list a couple of different mathematical methods for predicting NFL games, that I’ll start sharing here. These are based on Yards Per Point and Yards Per Play.

Teams | Yards Per Play | Yards Per Point |
---|---|---|

Colts -1 | Colts by 1 | |

Titans | Titans by 5.5 | |

Washington | ||

Detroit -4.5 | Lions by 1.5 | Lions by 5 |

Houston | ||

Cleveland -3 | Cleveland by 2 | Cleveland by 2 |

Jags | ||

Packers -13 | GB by 8 | GB by 7.5 |

Eagles -3 | Eagles by 2 | |

Giants | Giants by 3 | |

Tampa Bay -6 | Tampa by 7 | |

Carolina | Carolina by 2 | |

Denver | ||

Las Vegas -4 | Raiders by 2 | Raiders by 4.5 |

Bills | ||

Cardinals -2.5 | Arizona by 4.5 | Arizona by 4.5 |

Chargers | Chargers by 1.5 | |

Miami -1.5 | Miami by 20 | |

Bengals | ||

Pitt No line | Pitt by 9.5 | Pitt by 8.5 |

Seattle | Seattle by 5 | |

Rams -1.5 | Rams by 5 | |

San Fran | ||

Saints -9.5 | New Orleans by 3 | NO by 2 |

Baltimore -7.5 | Balt by 14 | Raven by 2.5 |

Patriots | ||

Vikings -2.5 | ||

Bears | Bears by 2.5 | Bears by 2.5 |

Now, in the games where both methods agree, we have Houston, Jacksonville, New York Giants, Arizona, San Francisco and Chicago. The 49ers are having some quarterback issues, so I might be inclined to drop them. If the number reaches 10, you might have to reconsider.

## NFL Betting Market Report 11-8-20

We’ll take a quick look this morning at what’s transpiring in the NFL betting markets.

**Denver at Atlanta:** The Falcons opened -4 and the line is still there with Atlanta getting 55% of the bets. Pinnacle has it at 3.5, so a Pinnacle lean to the Broncos.

**Baltimore at Indianapolis:** The Ravens have moved from -3 to -1 in this one even though they’re getting two-thirds of the wagers. I have each team winning by 2 points with the Yards Per Play and Yards Per Point methods.

**Houston at Jacksonville:** The Texans opened -7 and the number has moved to 6.5 even though Houston is getting 60% of the wagers. Both methods lean to the Jags in a bit of a surprise.

**NYG at Washington:** The home team is favored by 2.5 here and I have them winning by 1 with both methods. Not really enough variance for a play. Washington opened 3.5 and the betting has been split. Lean to the road dog here.

**New Orleans at Tampa Bay:** Big move towards the under here, as the number opened 55.5 and is now 50.5. Roughly 56% of the wagers are on the over. Could be some rain and wind, so would wait until closer to kick-off.

## College Football Market Report 11/7/20

Had some trouble logging on to the system after a security upgrade, but was able to get through a different way. Have a few updates, which I’ll get to in another post, but wanted to take a quick look at the college football betting market today before it gets too late.

**Pittsburgh at Florida State:** Haven’t really heard much about this game from bettors, but a pretty significant move towards the Seminoles, who are now favored by 2 after Pitt opened -2.5 and the betting has been pretty even.

**Troy at Georgia Southern:** Big move towards Troy, who opened as small dogs and are now favored by 3.5 after getting less than 50% of the wagers in the game.

**Kansas at Oklahoma:** The total here opened 66.5 and is now down to 63.5 even though 70% of the wagers have been on the over. Would usually look to take the under, but on the over in this one myself.

**Florida at Georgia:** Georgia opened -6 and the line is all the way down to 3 even though the betting is pretty equal.

## NFL Betting Report 10-18-20

We’ll use the same premise as Saturday, just looking at lines and moves based on betting percentages in the NFL.

**Houston at Tennessee:** Tennessee is now favored by 3.5 in this one, although it’s the total that draws a little attention. The number opened 55 and is now 53 with close to 60% of the bets on the over. Would take the under in this one.

**Baltimore at Philadelphia:** Both the total and side have seen movement here. The total opened at 48.5 and is now 46.5 with 70% of bets on the over. Pinnacle has the total at 46, so will grab the under in this one.

**Rams at 49ers:** Huge move here has the Rams favored by 3, which is a bit of overreaction. Would take 49ers in this spot.

**Kansas City at Buffalo:** The total moved from 55 to 57 even with more wagers on the under. As a result, would take the over in this one.

## College Football Betting Report 10-17-20

We’ll do something we did a few years ago, which is looking at the college football odds from all the sportsbooks, total wagers and Pinnacle and determine where the sharp money was coming from.

**Pittsburgh at Miami:** We’ve seen a pretty strong reverse move in this one, as the Hurricanes opened -9.5 and are now up to -13 or 13.5 depending on your sportsbook. A little sharp money on the ‘Canes in this one.

**Auburn at South Carolina:** Auburn opened -3 and the line went up to -3.5 before dropping back down to 3. Pinnacle has this one at 2.5 (-117) and coming off a key number is a bit unusual, so a slight lean to South Carolina in this one.

**Liberty at Syracuse:** Liberty is favored by 2.5, which is right where this one opened, but Pinny has moved the road team to -3, so a Pinnacle lean on the away favorite.

**Central Florida at Memphis:** This one is much like the game above, with CFU favored by 2.5 at most places and Pinnacle having bumped the number to Central Florida -3, so another lean to the road favorite.

Georgia at Alabama: Alabama opened -7 and the line dropped to 5.5 at most places even though the Tide are getting close to 60% of the wagers. Pinnacle has the number at Tide -6, which pretty much negates the reverse move.

## WNBA Season Wrap-Up

A bit of a disappointing WNBA season, as I started a little late due to the uncertainty of what was taking place and then sputtered a little bit, ending the regular season a game a game under .500 at 13-14. Did go 8-2 in the playoffs to end up with a 21-16 record, which is 56.75%, a bit lower than the two previous seasons, but good enough for a small profit. Our two-year record is 53-36, which is 59.55%.

We did have Seattle to win the championship back at the beginning of the season, so from a net unit standpoint, it wasn’t bad at all. Just wish we had fared a little better in the regular season.

By the time the 2021 season rolls around, hopefully things will be back to normal and we have games in front of the fans, or lack of in some cases in the WNBA.

## NBA Betting

*As mentioned, this is the first draft of the NBA chapter of my book on basketball betting. If you have any questions or comments feel free to shoot me an email at first name@first name/last name.com – (don’t want spam bots picking it up).*

I didn’t know it at the time but was actually a bit of a trend-setter when I first wrote Becoming a Winning Gambler in 2013. I wrote about some different systems and methods for handicapping basketball, which really wasn’t ideal for the type of book it was intended to be. But one of them was a short section on using Points Per Possession as a handicapping method. The one problem with the original Points Per Possession method is that it was one of those methods that was really too time consuming to do on a consistent basis and involved a fair amount of number crunching.

I’ll give the original the original concept here, as it does serve as an excellent lead-in to the now-popular Offensive and Defensive Efficiency ratings, which are really just Points Per Possession in a fancy box.

### Points Per Possession

One area that has shown some potential is points per possession, which is a bit of a spinoff from football’s yards per point. In points per possession, you’re looking to see how many points each team scores on a typical possession. This is a better indicator of a team’s true offensive capabilities than scoring averages. A run-and-gun type of team that averages 103 points per game isn’t nearly as effective as a slow, methodical team that averages the same 103 points per game, since the run-and-gun team is more likely to attempt a greater number of shots.

Points per possession can also be used to measure a team’s defensive ability, as you can calculate how many points a team allows on a typical possession. The run-and-gun team could be a better defensive team than the slower-paced squad, even though they will likely give up more points per game due to its style of play.

According to the NBA’s website, a possession can only end in one of four ways. A team can make a shot, miss a shot and not get an offensive rebound, have a turnover, or shoot free throws and either makes the last free throw attempt or doesn’t get the rebound if the last shot is missed. If the shooting team were to get the rebound, it would qualify as a new possession.

Unfortunately, there is no way to tell the exact number of possessions a team has in a game due to free throws. A player may be fouled while shooting a 3-point field goal and will get three foul shots instead of two and there are also technical fouls to consider. The NBA studied free throws and found that 43.6% of foul shots ended a possession and recommend using that percentage as a multiplier on the number of free throws taken; meaning a team that attempted 30 free throws would be given credit for 13.08 possessions, as 30*.436 =13.08.

The formula for finding a team’s points per possession is:

(Field Goals Attempted – Off. Rebounds) + Turnovers + (Free Throw Attempts*.436).

We’ll use the exhibition game between the Miami Heat and the Los Angeles Clippers from Oct. 11, 2012 as an example. In the 94-80 Miami victory, the Heat attempted 77 field goals and had nine offensive rebounds. Miami had 12 turnovers and attempted 26 free throws. For the Heat, we would have the following calculations: (77-9) + 12 + (26*.436) or 68 + 12 + 11.33 = 91.33. As the Heat scored 94 points, simply divide 94 by 91.33 to get a figure of 1.03, which is Miami’s points per possession for the game.

The Clippers scored their 80 points on 72 field goal attempts and had 16 offensive rebounds, while committing 25 turnovers and attempting 27 free throws. Therefore, the Clippers line would read: (72-16) + 25 + (27*.436) or 56 + 25 + 11.77 = 92.77. If we divide 80 points by 92.77 possessions the Clippers would get a .86 for points per possession.

You can also calculate a team’s defensive points per possession rating in the same manner, although you’ll be using statistics for the opposition. Ideally, a team will have a higher offensive points per possession than it does for defense.

### Using PPP for Handicapping Purposes

After 10 games are played, you can calculate an offensive and defensive points per possession number for each team. A number of websites, such as Yahoo, will have per-game averages already calculated for you, which will make the next step even easier and that is to calculate the average number of possessions in each team’s games. In the example above, the Clippers and the Heat saw a total number of 194 possessions. This helps you identify what type of pace a team likes to play.

Using stats from the 2011-12 regular season for a projection of a game between the New Orleans Hornets and the Los Angeles Lakers. The Hornets averaged 89.6 points per game and attempted 77.3 field goals per game, while having an average of 11 offensive rebounds. They made 14.6 turnovers per game and attempted 21.2 free throws per contest. The line for the Hornets would read (77.3-11=66.3) + 14.6 + (21.2*.436=9.24) = 90.14, meaning the Hornets average 90.14 possessions per game. Divide 89.6 points per game by 90.14 and you get an offensive figure of .99.

Defensively, the Hornets allowed 93.4 points per game and saw the opposition attempt 78.6 field goals and make an average of 11.1 offensive rebounds per game. The opposition also committed 13.2 turnovers per game and attempted 22.5 free throws a game. The defensive line for the Hornets would be (78.6-11.1=67.5) + 13.2 + (22.5*.436=9.81) = 90.51, meaning the Hornets allow an average of 90.51 possessions per game. Divide 93.4 points per game by 90.51 and New Orleans would have a defensive figure of 1.03, while seeing an average of 180.65 possessions in their games.

For the Lakers, they averaged 97.3 points per game on 80.6 field goal attempts and 12.1 offensive rebounds. Los Angeles committed an average of 14.5 turnovers per game and attempted 24.1 free throws, which would give a line of: (80.6-12.1=68.5) + 14.5 + (24.1*.436=10.51) = 93.51, which the average number of possessions the Lakers have per game. Divide 97.3 by 93.51 and the Lakers would have an offensive points per possession rating of 1.04.

The Lakers allowed 95.9 points per game on an average of 86.4 field goal attempts and 11.5 offensive rebounds. The Lakers’ opposition made and average of 10.9 turnovers per game and attempted 18.4 free throws, which would give Los Angeles a defensive rating of: (86.4-11.5=74.9) + 10.9 + (18.4*.436=8.02) = 93.82, which is the average number of possessions the Lakers allow per game. Divide 95.9 points per game by 93.82 and the Lakers get a defensive figure of 1.02, while seeing an average of 187.33 possessions in their games.

The first step is to take the Hornets offensive rating and add it to the Lakers defensive rating and divide by two, which in this case is (.99+1.02)/2=1.005. Next, take the Lakers offensive rating and add it to the Hornets defensive rating and divide by two, which will be (1.04+1.03)/2=1.035.

The third step is to take add both team’s average number of possessions and divide by four, which is how many possessions each team is expected to have during the game. In this case we will have (180.65+187.33)/4=91.995.

Multiply the number of expected possessions by each team’s new offensive rating. For the Hornets, you would multiply 1.005 by 91.995 to get an expected total of 92.46 points. For the Lakers, 1.035 multiplied by 91.995 gives an expected total of 95.21 points.

You’re not quite done yet, as the final step involves subtracting two points from the road team and adding two points to the home team’s score to account for home court advantage. Simply adding four points to the home team’s score will throw off the predicted total, which is why two points are subtracted and added.

At first glance, I thought the predicted margin between the two teams was quite a bit low, as the Hornets were one of the worst teams in the NBA during the 2011-12 season, but when I looked at the results between the two teams, the projections weren’t off as much as I thought. The Hornets went 2-1 against the spread when playing the Lakers, losing by two points and by three points. The lone game they didn’t cover was a 107-101 home loss in overtime as 4.5-point underdogs. The teams were tied at 93 at the end of regulation. Los Angeles won 88-85 at home as 12-point favorites and came away with 93-91 road victory as 3.5-point favorites in the other two matchups.

Points per possession is a relatively new concept, at least as it applies to sports betting. But it does appear to have some possibilities and could be something you want to include in your handicapping arsenal.

As you can see, there were a lot of calculations involved and the number of projected possessions per game wasn’t quite as accurate as it could be, as it failed to incorporate the average number of possessions on a league-wide basis, which I call Differential From League Average.

But all of those calculations are now done for you by the league and many other sports websites under the names Offensive Efficiency and Defensive Efficiency.

### Differential From League Average

Before we look at using the efficiency ratings to create projected scores, we’ll look at the concept of Differential From League Average, or DLA for short. What DLA does is give you a barometer to measure numbers against, as they apply to handicapping. Knowing an NBA team averages 100 possessions a game doesn’t really tell you a whole lot regarding if they are a fast-paced team or not unless you can compare it with something. In this case, it’s the league average of possessions that you will compare the 100 possessions to. But DLA can also be used for scoring averages and other facets of the game, as we’ll see.

In the 2019-20 NBA season, the Golden State Warriors scored 106.3 points per game and allowed 115, while the Orlando Magic scored 106.4 and allowed 107.3. If asked to predict a score, most people would take the scoring average of one team and add it to the defensive average of the other team and divide by two. Golden State would have a predicted score of 106.8, which is 106.3 + 107.3 divided by two, while Orlando would have 110.7, which is 106.4 + 115 divided by two.

The one problem with the above projection is that there is nothing to factor in that the Warriors are a poor offensive team and the Magic are a fairly good defensive team. What we do now is introduce the league average of 111.4 points so that we have something to compare the numbers to and you see that Golden State averages 5.1 fewer points than the league average.

Orlando allows 4.1 fewer points than the league average, so is it reasonable to expect the Warriors to score 106.8 points, which is 4.6 fewer points than the league average? Remember, Golden State averages 5.1 fewer points than the league average overall and now they are playing a good defensive team, so it doesn’t make sense to expect them to score more points than they typically do.

This is where the concept of DLA comes into play and what you do is weigh each number against the league average. We just saw where Golden State gets a -5.1 for offensive scoring and Orlando receives a -4.1 for allowing 4.1 fewer points than an average team. When added together you get a -9.2, so Golden State is predicted to score 9.2 fewer points than the league average. With the league average at 111.4 points when you subtract 9.2 you’re left with 102.2 points, which is our prediction on how many points the Warriors will score.

For Orlando, the Magic score -5.0 fewer points than an average team, while the Warriors allow 3.6 more points, so Golden State receives a +3.6 for its defense. When added together, the Magic are expected to score 1.4 fewer points than the league average of 111.4, which is 110 points, so our projection is Orlando 110-102.2 or 110-102.

That’s just a quick work-through using scoring averages, but as mentioned earlier the concept of DLA can be applied to different aspects of the game and for our purposes, we’ll be using DLA for both pace numbers and the efficiency ratings.

When I refer to league averages, you can also use league medians. I use medians rather than averages frequently, as medians are much quicker to calculate. If you remember from your high school math days, mean is average, while median is the number right in the middle, which in the NBA is the average of teams No. 15 and No. 16, since there are 14 teams higher and 14 teams lower. With most online stats sortable, you can obtain a median in a matter of seconds, as opposed to spending the time adding up numbers for all of the teams and then diving by 30.

### Efficiency Rating Projections

Efficiency ratings have become a bit of craze when it comes to advanced statistics in the NBA. But all they really are is Points Per Possession multiplied by 100. That’s it. After seeing all the calculations that had to be done before efficiency ratings became so widely available, we should be grateful that the basic concept has caught on, as now we can do game projections in just a couple of minutes by using those numbers.

While there are plenty of different sites which list efficiency ratings, they are not all the same, as there is a bit of disagreement when it comes to the formula and free throws, as some use the .436 and others use .44, which will throw the numbers off slightly. There is also a bit of a difference in the pace ratings between sites, which is also dependent on the formula used, among other things.

I prefer to use the official NBA stats at www.nba.com, which lists both pace and efficiency ratings on the same page. But the primary reason I use the NBA site will become apparent in a little bit, as using the efficiency ratings for projections is essentially a two-part process. For the 2019-20 season through the shutdown, the average pace was 100.5 possessions, while the average Offensive Efficiency was 110.4 and the number for Defensive Efficiency was 109.9. The NBA calls the two Offensive Rating and Defensive Rating.

The first step in creating a projected score is to come up with the pace rating for the game and we’ll use a game between Memphis and Philadelphia to work through. Memphis averaged 103.3 possessions per game, while Philadelphia had an average of 99.4 possessions. The Grizzlies see 2.8 more possessions than average, and Philadelphia sees 1.1 fewer possessions than average. Memphis will get a +2.8 for pace and the 76ers receive a -1.1, so you’re left with a +1.7 for pace. Adding 1.7 to the average pace of 100.5 gives you 102.2, so we can expect 102 possessions per team in the game.

Memphis has an Offensive Efficiency rating of 108.9, which is -1.5 compared to the league average and Philadelphia has a Defensive Efficiency rating of 107.6, which is -2.3 less than the league average, so Memphis will get a -3.8 for this game. When you take 110.4 and subtract 3.8, you’re left with a total of 106.6, which is the final adjusted rating for Memphis.

Philadelphia has a 109.7 for its Offensive Efficiency, which is -.7 from the league average and Memphis has a 109.9 for defense, which is the same as the league average, so no adjustment needs to be made there. Philadelphia’s only adjustment is a -.7 from the league average of 110.4, which leaves you with 109.7, which also happens to be Philadelphia’s Offensive Efficiency rating. That makes sense since Memphis is your average defensive team.

Now we’re left with adjusted numbers of 102 pace possessions per game and ratings of 106.6 for Memphis and 109.7 for Philadelphia. The last step before being able to calculate a projected score is to turn the efficiency rating for each team into a points per possession rating, which is simply moving the decimal point two places to the left. All we’re doing is dividing by 100, so moving the decimal point is all that’s needed.

You’ll simply multiply the expected number of possessions of 102 by 1.066 for Memphis and by 1.097 for Philadelphia to get a projected score, which in this case is 111.89 for the 76ers and 108.73 for the Grizzlies, so we have Philadelphia 3.16 points better and a projected total score of 220.62 on a neutral court.

The home court advantage isn’t as great as some people tend to believe, as home teams had a scoring margin of +2.1 in the 2019-20 season, so add one point to the home team’s projected score and subtract a point from the visiting team’s projected score to account for home court. Simply adding two points to the home team will throw off your projected total, which is why the addition and subtraction are done.

If you have a little bit of Excel knowledge, you can easily create a spreadsheet where all you do is enter the league average for pace and Offensive and Defensive Efficiency, along with the numbers of each team to get your projections and it will save a little bit of time.

Let’s work through one more just to make sure you have the process down and this time we’ll look at the game between New York and Atlanta, which was one of the four games played on March 11, 2020, the last day of games before the league shutdown.

New York entered the game averaging 99.1 possessions, along with an Offensive Efficiency of 105.9 and a Defensive Efficiency of 112.4. The Hawks averaged 103.3 possessions and had an Offensive Efficiency of 107 and a defensive rating of 114.4.

For possessions, New York receives a -1.4, while Atlanta receives a +2.8, giving a result of +1.4, which will become 102 possessions per team, since the +1.4 is added to the league average of 100.5, for a total of 101.9 and we’ll simply round up.

New York’s offensive rating of 105.9 is -4.5 compared to the league average, but Atlanta’s defensive number is 4.5 points worse than the league average, so New York receives a -4.5 and a +4.5 to give them a 0, meaning they will get the league average of 110.4.

Atlanta’s offensive number is -3.4 compared to the league average, but New York is 2.5 points worse on defense, so the Hawks receive a -3.4 and a +2.5 for the Knicks’ defense, which results in a -.9 rating for Atlanta in this one, which is a 109.5.

Multiply the 102 possessions by 1.104 to come up with New York’s projected score and multiply 102 by Atlanta’s 1.095 to come up with the Hawks’ projection. New York’s projection is 112.6, while the Hawks receive a 111.7. On a neutral court, we’d have New York winning by one, but with the Hawks being at home, Atlanta would be projected to win by a point and a total of 224.3.

The Hawks were favored by 4.5 and the total was 232.5, so we’d be looking at the Knicks and the under and would have split on this game, as the teams were tied 118-118 at the end of regulation and Atlanta went on to win in overtime.

Seeing the game go over the total wasn’t really a surprise, as the Hawks had seen a big increase in scoring recently. For the season, the Hawks averaged 111.8 points per game and allowed 119.7, but if you look at their games from February and March, you see Atlanta averaged 118.7 and allowed 124.3.

The Knicks also had been on a bit of a scoring increase, as New York averaged 105.8 points and allowed 112.3 for the season, but in February and March the Knicks were scoring 110.8 points and allowing 113.6.

That happens more frequently than expected, and the only way to deal with it is to look at a team’s most recent performances. What we do is run a second set of numbers, but instead of using season-to-date averages, we will use numbers from a team’s last 10 games, which is easily available on the NBA site with a click of a button and is the primary reason I use the nba.com website. Under the “Season Segment” just click last 10 games and you get an entirely different group of numbers, which consists of the last 10 games for each team.

You do have to start from scratch, so for the last 10 games, we’ll have an average pace of 100.6, and an average offensive number of 111.5 and a 112.9 for defense.

In the last 10 games, New York has an average pace of 98.7 and the Hawks are seeing 102.1 possessions, so New York receives a -1.9 for pace and Atlanta gets a +1.5, so we’re left with a -.4. If you take 100.6 and subtract .4, you’re left with a 100.2, which we’ll round down to 100. Our expected possessions are now 100 based on the last 10 games.

But we see both teams have really picked up the offense, as New York has an offensive rating of 112.4 and the Hawks have a 113.4. On defense, the Knicks have a Defensive Efficiency of 116.3 and the Hawks receive a 117.

New York’s 112.4 is +.9 better than the league average and Atlanta’s defensive number is 4.1 points worse than average, so the Knicks receive a +.9 and a +4.1 for a total of +5. Atlanta’s offense gets a +1.9 and New York’s defense receives a +3.4 for a total of +5.3.

For the Knicks, 111.5+5=116.5, which is New York’s projected score, and the Hawks end up with 111.5 plus 5.3 for a total of 116.8. We now have Atlanta winning by .3 points on a neutral court and a total of 233.3. Accounting for home court, our line on this one would then be Atlanta -2.3 and a total of 233.5.

Redoing the numbers a second time isn’t too time consuming, as it’s a relatively easy process, but is something that should be done to enable you to keep up with changes in style. If you only have time to run one set of numbers, I’d be more inclined to use the most recent numbers but ideally, you’ll be able to run both sets to get the best projections. The key is to find games where both the season and the recent numbers agree on the same side or total.

Looking at a team’s numbers for the most recent 10 games allows you to pick up on trends that the general betting public may have overlooked, as was the case with the 2019-20 Milwaukee Bucks, who were one of the best offensive teams in the league. But in the final 10 games before the league shutdown, Milwaukee ranked dead last in Offensive Rating, but were easily No. 1 in defense. Despite being tied for No. 2 in pace, Milwaukee was 2-8 in totals over that span, thanks to some poor shooting and solid defense.

At the other end of the spectrum was the Orlando Magic, who were primarily a defensive team and scored and allowed fewer points than the league average. But the Magic flipped the switch shortly before the shutdown and were No. 1 in offense and No. 25 defense for the last 10 games. As a result, the Magic finished with 12 straight overs before the shutdown occurred.

### SDQL

Before getting to the next method, I’ll briefly touch on SDQL, which stands for Sports Database Query Language. A little bit of SDQL knowledge will save you quite a bit of time with several of the methods we’ll be looking over in the rest of the chapter.

There are several different websites which contain SDQL, such as sportsdatabase.com and killersports.com. I typically use sportsdatabase.com, but either one is fine. There is some solid tutorial information on the killersports.com site, and you should spend a little bit of time reading over that.

It’s not all that complicated to learn the basics, which is all you really need for our purposes. For those who don’t want to read-up on SDQL, I’ll list the basics for each query, so you can get the numbers you need, but don’t have to put in any more time. But it’s definitely to your benefit to have a little knowledge of the SDQL process and it is one of the best free resources out there for basketball bettors.

### Simple Math Method

If I had to pick one statistical method that is the basis for my handicapping, it would be this one, which I use for both basketball and football. It’s another method that incorporates the Differential From League Average theory. For lack of a name, I’ll just call it “Simple Math Method,” which is probably as good as anything. There are minor modifications made depending on the sport, as you’ll see when we look at the NBA, college basketball and WNBA.

For the NBA, all you need is the average team score and the home or away scoring of each team in the game. This is another method where it’s best to run the numbers twice, once for the season and again to look at a team’s more recent efforts.

At the time of the COVID-19 stoppage in the 2019-20 NBA season, the average number of points was 111.4 per team. (The SDQL query for this is simply “season=2019”).

We’ll use the same New York at Atlanta game that was used earlier for the efficiency ratings. For the season, the Knicks averaged 106.2 points on the road and allowed 114. Atlanta averaged 114.6 points and allowed 117.4. (SDQL queries here are “season=2019 and team=Knicks and A” and also “season=2019 and team=Hawks and H”).

The first step is to compare both teams’ averages against the league average and come up with offensive and defensive percentages. New York is scoring 106.2 points per game on the road, which divided by 111.4 gives an offensive percentage of .95, while Atlanta is scoring 114.6 points per game at home, which divided by 111.4 gives an offensive rating of 1.03.

For defense, New York is allowing 114 points per game on the road, which divided by 111.4 yields a defensive rating of 1.02. Atlanta is allowing 117.4 points per game at home, which divided by 111.4 yields a defensive rating of 1.05.

The next step is to add New York’s offensive rating of .95 to Atlanta’s defensive rating of 1.05 and then subtract one, giving a figure of 1.00. Do the same for Atlanta, using its offensive rating of 1.03 and New York’s defensive rating of 1.02 to get a figure of 2.05, which becomes 1.05 after you subtract one.

Next, multiply New York’s figure of 1.00 by the league average of 111.4 to get a predicted score of 111.4 points. Multiplying Atlanta’s 1.05 by the league average of 111.4 yields a projected score of 116.97, or 117 points. So, we basically have Atlanta predicted to win by 5.5 and a total of 228.5. Since home and away numbers are being used for the projections, there’s no need to make any home court adjustment.

But we’re not finished yet, as there are still the more current numbers to look at. While the efficiency ratings use the last 10 games to look at current scoring, this method has a little more leeway, since it’s easiest to get recent scoring numbers in terms of months. In this game, I’ll use New York’s away scoring for February and March, which consisted of nine road games. The same time span shows 10 home games for Atlanta.

Since we are now looking at February and part of March as our time frame, the first step again involves coming up with the league average in scoring and it’s now up to 112.9, as scoring was up in the league for the final six weeks before the shutdown. Atlanta and New York both did their part to help with the scoring increase, as the Knicks averaged 114.6 points on the road and allowed 116.6 during that time. The Hawks averaged 124.7 in those 10 games but allowed 125.5.

(The SDQL query for this is a little more extensive but is still pretty simple. Since we are interested in February and March numbers only, we need to change the season parameters slightly, so our query is going to be “season=2019 and month>1 and month<4” which says you are interested in months 2 and 3 only, which are February and March. Your team queries will now be “season=2019 and month>1 and month<4 and team=Knicks and A” and “season=2019 and month>1 and month<4 and team=Hawks and H”).

Now, we have New York’s offensive number of 114.6 divided by 112.9 giving a 1.02 and New York’s defensive average of 116.6 gives the Knicks a 1.03 on defense. The Hawks receive a 1.10 on offense and a 1.11 on defense. New York’s offensive number of 1.02 added to Atlanta’s 1.11 gives a total of 2.13 and we’re left with a 1.13 after subtracting 1. For Atlanta, the Hawks’ 1.10 on offense is added to New York’s 1.03, leaving the same 2.13, which becomes 1.13 after subtracting 1.

We can quickly tell that we will have the game even, as both teams have the same final number, and the projected points for each team is 127.57, so we have this one going well over the total, and the game even, so would have been more than happy to see the 118-118 score at the end of regulation.

But as you can see, both methods definitely benefitted from looking at the most recent scoring trends, as both teams had picked up the pace a bit and handicappers using just season stats would not have picked up on that.

It does involve a little more time but isn’t too terribly bad and is another method that can be set up in Excel to perform the calculations for you once you input the scoring numbers.

### Injuries/Suspension

One problem that all handicappers run into is that your numbers assume that each team is going to be healthy and not missing anybody due to injury, suspension, or as is becoming more common, rest. This isn’t anything unique to basketball, as it occurs in all sports. There’s really no universal way of dealing with injuries and how it will impact a team. A lot of it is team specific and depends on how the team is constructed. A team that is weak at one position will miss an average starter more than a team that has a solid backup. That’s common sense.

Injuries probably don’t have as much of an impact in the NBA as they do in other sports since teams are accustomed to playing without their best players for stretches of time during a game. Milwaukee’s Giannis Antetokounmpo was averaging 31 minutes a game prior to the league shutdown in 2019-20, so the Bucks are without him on the court 17 minutes a night on average. Playing without the big guy will definitely hurt the Bucks, but it’s not as though Milwaukee will be completely lost without him on the floor.

The maximum adjustment on the line for a star player missing a game is roughly 3-4 points. There aren’t many players that are worth that much of an adjustment. For oddsmakers, they have to factor in both the player’s impact on the team, but also account for public perception a bit in adjusting the odds when a player is out. Some players are going to be overrated by the betting public a bit and much of that has to do with scoring. A player who puts up a lot of points, but does nothing else well, is likely to be over-valued a bit compared to a well-rounded player, who doesn’t put up the same number of points, but does the other things well.

If you want to use a mathematical method of accounting for injuries, I’d use the player efficiency ratings on the NBA’s website. The leader for the 2019-20 season at the time of the shutdown was Giannis Antetokounmpo with a rating of 34.8. What you will do is take the NBA’s rating and divide by 10, which is just moving the decimal point one spot to the left, so your 34.8 becomes 3.48. The league average is 11.5, and that’s also divided by 10, making it 1.15.

Subtract 1.15 from 3.48 to get a result of 2.33, which is multiplied by two, giving you a final number of 4.66 points. The final step is to simply drop the numbers after the decimal, which in this case leaves a 4-point adjustment for Milwaukee without Antetokounmpo, which is a pretty reasonable estimate on how the Bucks will perform without their big man.

Looking at the ratings through the 2019-20 shutdown, shows Antetokounmpo and James Harden as the two players who earn four-point adjustments for the absence, while players like Anthony Davis, LeBron James, Luca Doncic, Damian Lillard, Khawni Leonard, and Russel Westbrook received a three-point adjustment.

Bradley Beal was the second-leading scorer in the league with 30.5 points a game, but scoring is about the only thing he did, leaving him with an efficiency rating of 25.4, which after our adjustments made him worth two points. Players like Zach LeVine and Donovan Mitchell were both in the top 15 in scoring, but only receive a 1-point adjustment due to their all-around play.

For players with a rating of 16.5 or less, there is no adjustment and a number of players will fall into this category. A quick glance will let you know you that you can discount their absence and quickly move on.

Basically, injuries are worth anywhere from 0 to 4 points using this method, which isn’t perfect, but does give you a reasonable estimate of how a player’s absence will affect his team.

### Strength of Schedule

Strength of schedule, or Average Opponent Power Rating (AOPR) is a key concept in college sports, where teams playing vastly different levels of competition, but isn’t really a factor in professional sports. Sure, there are going to be some teams who have played slightly tougher slates than others, but the difference is minor and isn’t worth the time or effort to factor into your handicapping. As a rule, extremely weak teams will get credit for playing a tougher schedule than your very good teams, simply due to playing one another. The weak team gets credit for playing a top-notch team, while the good team is penalized a bit for playing a weak foe.

Using Jeff Sagarin’s NBA ratings for strength of schedule, showed New Orleans with the toughest schedule and an Average Opponent Power Rating of 90.87 and the Washington Wizards at No. 30 with a rating of 89.19, making the difference rather miniscule.

For comparison, Sagarin’s college basketball ratings had Gonzaga as the third-best team in the country, but the Bulldogs were No. 81 with an AOPR of 75.82. Michigan State was right behind Gonzaga in the power ratings, but the Spartans were No. 3 in AOPR with an 82.99, which is a significant increase from Gonzaga. The difference between the team with the toughest schedule, Kansas (83.58) and the weakest, Morgan State (62.37) is 21.21 points.

### First Half/First Quarter Bets

While I’ve had some success with first-half bets in college basketball that hasn’t really transferred over to the NBA, but in all fairness, I only tracked them for a couple of days, which is a mistake on my part. That’s primarily due to time constraints in trying to get everything else done in a timely manner each day. While the majority of college teams follow a formula of scoring more in the second half of games, and that is reflected in the line, NBA teams are more balanced, with some scoring more in the first half and others doing the majority of their scoring in the second half and that’s reflected in the line.

Depending on a team’s scoring trends, first-half totals can be more or less than half the game total. It isn’t unusual to see 112.5 for a first-half total where the full game number is 220 if the teams show a tendency to score more in the first half.

The 2019-20 Washington Wizards averaged 58.8 points in the first half and allowed 63.1, while only scoring 56.3 in the second half and giving up an average of 55.9. Most of that is likely due to opposing teams having bench players in the game. The Milwaukee Bucks averaged 59.2 points in both the first half and second half, which allowing 52.7 points in the first half and 54.4 in the second half.

Certain teams also show specific tendencies in the first quarter, such as the Oklahoma City Thunder, who were 40-24 at the time of the shutdown, but were outscored 26.7-27.2 in the first 12 minutes of games. Denver was another good team who was outscored in the first quarter and also had their highest-scoring quarter but allowed 28.1 points. The Nuggets didn’t allow more than 26.5 points in any of the other three quarters.

If you want to run numbers for the first half or the first quarter you already know the formula, which is just the Simple Math Method and using just first half points or first quarter points. When you run the SDQL queries mentioned you get not only total points, but you see a scoring breakdown for each quarter.

Let’s work through one for the first half, which will simply be a copy and paste of the full game projections but I’ll plug in first half and first quarter numbers instead of the full game scoring averages.

At the time of the COVID-19 stoppage in the 2019-20 NBA season, the average number of first half points was 56.0 points per team. (The SDQL query for this is simply “season=2019”).

We’ll use the same New York at Atlanta game that was used earlier for the efficiency ratings. For the season, the Knicks averaged 52.9 first half points on the road and allowed 57.7. Atlanta averaged 55.4 first half points and allowed 60.1 at home. (SDQL queries here are “season=2019 and team=Knicks and A” and also “season=2019 and team=Hawks and H”).

The first step is to compare both teams’ averages against the league average and come up with offensive and defensive percentages. New York is scoring 52.9 first half points on the road, which divided by 56 gives an offensive percentage of .94, while Atlanta is scoring 55.4 first half points at home, which divided by 56 gives an offensive rating of .99.

For defense, New York is allowing 57.7 first half points on the road, which divided by 56 yields a defensive rating of 1.03. Atlanta is allowing 60.1 first half at home, which divided by 56 yields a defensive rating of 1.07.

The next step is to add New York’s offensive rating of .94 to Atlanta’s defensive rating of 1.07 and then subtract one, giving a figure of 1.01. Do the same for Atlanta, using its offensive rating of .99 and New York’s defensive rating of 1.03 to get a figure of 2.02, which becomes 1.02 after you subtract one.

Next, multiply New York’s figure of 1.01 by the league average of 56 to get a predicted score of 56.6 points. Multiplying Atlanta’s 1.02 by the league average of 56 yields a projected score of 57.1 points. So, we basically have Atlanta predicted to lead at halftime by .5 and a total of 113.7. Since home and away numbers are being used for the projections, there’s no need to make any home court adjustment.

But we’re not finished yet, as there are still the more current numbers to look at. While the efficiency ratings use the last 10 games to look at current scoring, this method has a little more leeway, since it’s easiest to get recent scoring numbers in terms of months. In this game, I’ll use New York’s away scoring for February and March, which consisted of nine road games. The same time span shows 10 home games for Atlanta.

Since we are now looking at February and part of March as our time frame, the first step again involves coming up with the league average in scoring and it’s now up to 57, as scoring was up in the league for the final six weeks before the shutdown. For February and March the Knicks averaged 56.7 first half points on the road and allowed 56.2. The Hawks averaged 60.4 first half points in those 10 games but allowed 61.7.

(The SDQL query for this is a little more extensive but is still pretty simple. Since we are interested in February and March numbers only, we need to change the season parameters slightly, so our query is going to be “season=2019 and month>1 and month<4” which says you are interested in months 2 and 3 only, which are February and March. Your team queries will now be “season=2019 and month>1 and month<4 and team=Knicks and A” and “season=2019 and month>1 and month<4 and team=Hawks and H”).

Now, we have New York’s offensive number of 56.7 divided by 57 giving a .99 and New York’s defensive average of 56.2 also gives the Knicks a .99 on defense after rounding. The Hawks receive a 1.06 on offense and a 1.08 on defense. New York’s offensive number of .99 added to Atlanta’s 1.08 gives a total of 2.07 and we’re left with a 1.07 after subtracting 1. For Atlanta, the Hawks’ 1.06 on offense is added to New York’s .99, leaving 2.05, which becomes 1.05 after subtracting 1.

New York’s 1.07 multiplied by the league average of 57 gives a projected first half score of 60.99, or 61, for the Knicks, while the Hawks’ 1.05 multiplied by 57 yields a projection of 59.85, which is basically 60 points. We have the Knicks leading 61-60 at the break. In the actual game, the Hawks could do nothing right in the first half, as New York led 67-50 at intermission.

The same concept applies to first quarter wagers, just replace the scoring numbers with first quarter stats. For the season stats, we would have the Knicks 28.46-27.06, which is basically New York by 1.5 with a total of 55.5 and the more recent numbers for games of February and March would have New York 29.92 to Atlanta’s 27.07, which is New York by 3 with a total of 57.

The Knicks led 33-24 at the end of the first quarter, as Atlanta’s slow start to the game began right from the opening tip-off.

### Halftime Wagers

There has been little, if anything, written about making halftime wagers in the NBA. But a quick glance at some scoring trends shows that it is something that probably warrants more examination. Looking at the 2019-20 Milwaukee Bucks, we’d see Milwaukee led by 10 or more points at the half 27 times before the shutdown. In those games, the Bucks were outscored in the second half by a 57.3-57.9 margin. When the Bucks led by 5 points or less, they outscored teams by an average of 57.3-46.5, so while the offense was the same, the Bucks really turned up the defense in the second half. The Bucks were 11-0 straight-up when leading by 5 or less at the half and 25-2 straight-up when leading by double digits at the break.

In the case of the Bucks leading by 5 points or less, not only could Milwaukee be a second half play, but the under also looks like it could be a decent wager depending on the number posted.

Milwaukee isn’t the only team to show some distinct tendencies. You have the Atlanta Hawks, who somehow managed to go 0-3 straight-up in the three games they led by double digits at the half. Atlanta was outscored 42.7-61.3 in the second half of those games.

The Lakers, Nuggets, and Pacers are other teams who were outscored in the second half when they had a double digit halftime lead, but then you have teams like the Clippers, who showed a killer instinct by outscoring foes 57.2-51.8 when leading by 10 or more at the half. Boston and Dallas also didn’t let up in the second half and extended their leads.

On the other side of the equation, there were some poor teams who showed a bit of fight when trailing by 10 or more points at halftime, with the Pistons outscoring foes 54.8-51.8 in the second half and Chicago having a solid 58.2-50.1 scoring advantage in the second half. Phoenix outscored foes 61.4-53.2 in the second half when trailing by 10 or more points.

Teams who were made favorites entering a game and found themselves trailing by 10 or more points at the half, outscored foes 56-50.3 in the second half, so the defensive effort was there. Teams who were underdogs outscored their opponents 56.1-55.3. Favorites of 5 or more points outscored their opponents 60-49.1 when trailing by double digits at the half, while underdogs of 5 or more points were outscored 55.2-56.3.

These types of trends are most likely ones that need to be looked at one a season-by-season basis, as opposed to teams continuing a specific type of trend for several consecutive seasons. Players are constantly changing teams and while the No. 8 and No. 9 players on a team may not receive a lot of attention when switching teams, they are the players who could have an impact on a second half wager depending on the situation and are more likely to see extended minutes when a team is up or down by 15 points.

There are definitely some interesting trends relating to individual teams and depending on the time you have available; second half wagers could be something worth looking into a little more. It is something few bettors partake in and a handicapper willing to put in the time and effort into second half trends and tendencies should have an advantage over the traditional second half player, who is looking to either hedge a full game bet or just get involved in a game they didn’t have a wager on.

### Trend Handicapping

As I mentioned, I’m typically not a huge fan of trend-oriented handicapping, but there a few exceptions, particularly when it comes to professional basketball. Some teams tend to play different depending on the outcome of its previous game. The 2019-20 Chicago Bulls were 7-13-1 ATS and 8-13 in totals after a victory, but 24-18 against the spread and 22-20-1 in totals after a loss. Chicago did have one of the most profound differences in the league, but it’s hard to ignore those differences. Oklahoma City was 12-11-1 ATS after a loss, but the Thunder were a solid 25-13-1 against the spread after a victory.

These types of trends don’t really carry over from season-to-season, so I’d use them for the current season only and this is another area where SDQL is a huge time saver. (The SDQL query is “season=2019 and team=Bulls and p:W” which shows you how Chicago did after a victory and “season=2019 and team=Bulls and p:L” which tells you how the 2019-20 Bulls did if the previous game was a loss. For Oklahoma City, you just change the team=Bulls to “team=Thunder”).

Another area worth looking at involves rest as it pertains to teams playing in one extreme or the other. Most NBA teams will have one or two days between games, so I look to see how teams perform with no rest and how they do when they are off for three days or more.

Using the Atlanta Hawks, when they had three or more days of rest, Atlanta was 3-3 straight-up, 4-2 against the spread and 2-4 in totals, as Atlanta averaged 110.5 points and allowed 111.7. Compare that to Atlanta’s record when playing with no rest, where the Hawks were 2-10 straight-up, 3-9 ATS and 8-4 in totals, as they averaged 106.3 points per game, but allowed 124.3.

The 2019-20 Milwaukee Bucks were the opposite, at least as it pertains to totals, as Milwaukee was 3-7 in totals with no rest and 3-1 with three or more days in between games, with the lone loss coming by a half-point.

Not all teams show much of a difference in the different categories and there is a part of it that’s simply random variation, but there are enough teams to show dramatic differences in how they play after a win or a loss to make it worth your while to track. Looking at points scored and allowed in each situation can give you a better idea if a team’s record is due to random variation or a difference in style, such was the case with the Atlanta Hawks and the rest factor.

In the 2018-19 NBA season, I used several different math-based methods for NBA totals and also factored in how teams performed after a win or loss and began the season in amazing fashion. It seemed like I couldn’t miss and was hovering in the 70% range for the first 50 or 60 plays. But what I didn’t do was incorporate any sort of additional factor to account for a team’s recent performances and stuck with season-to-date numbers, so naturally, things went downhill just as fast.

How teams perform in the different situations shouldn’t be the sole factor in determining a play, but it could be beneficial when you’re on the fence regarding a certain game or if there’s a televised game that you want to place a wager on.

One other area that I’ve dabbled with a bit is looking at how teams do when playing against certain totals that are outside of their average range by at least 10 points, although there is a bit of leeway there. An example would be the 2019-20 Brooklyn Nets, who saw an average total of 222.6 during the season. (SDQL is “season=2019 and team=Nets”). For the Nets, it would be looking to see Brooklyn fares in games where the total is 212.5 or lower, as well as how they do in games where the total is 232.5 or higher. (SDQL is “season=2019 and team=Nets and total<213” and “season=2019 and team=Nets and total>232”).

Running those two queries for Brooklyn would show the Nets were 2-1 ATS and 0-3 in totals when the total was 212.5 or less and 3-6 ATS and 3-6 in totals if the number was 232.5 or higher. This is really one of those things that I’d track only if time permitted, but there are enough trends that develop to make it worth looking at if possible.

### Situational Plays

The following systems first appeared in the Second Edition of Becoming a Winning Gambler, which is basically a crash course in which gambling games can be beaten and which ones are decided by luck. The majority of the book is devoted to sports betting and for each sport, a different handicapping method is given. For the NBA I decided to use a situational approach, with time constraints during basketball season one of the primary factors.

Here we’ll look at two different systems that have each won four of the past five seasons and between the two have gone 58-40-1 during that span, so you get about 20 plays per year.

Bet against any home underdog who has won at least four straight games. Dating back to the 1995-96 season, this system has yielded a 90-113-4 (44.3%) record, so betting on the opposition would have given a 113-90-4 record, which is 55.7%. That’s not a bad record for something that will take less than 30 seconds to check for each day. From 2015-16 through the 2019-20 seasons, the record going against these teams is 31-24-1 (56.4%) so just a shade better than the long-term results.

Bet on any away favorite who has lost at least four straight games. This one is 122-83-6 (59.5%) dating back to the 1995-96 season and has gone 27-16 (62.8%) from 2015-16 through 2019-20. Interestingly, if you look at away favorites who have lost at least three straight games, your record would be 284-215-11 (56.9%), but would yield a greater profit due to the extra number of plays. Between 2015-16 and 2019-20 the record has been 75-49 (60.5%) but part of that is due to the 2018-19 season where teams were 20-4 ATS.

If you subtract the 2018 season, you’re left with a 55-45 ATS record, which is still 55%. Away favorites who had lost at least four straight games were 8-2 ATS in 2018, so you would be 19-14 (57.5%) if you subtract that season.

It’s not entirely fair to subtract that season even though it was a bit of an anomaly regarding its record since you’re penalizing a system for being so successful. But it also gives you a better indication of what you can expect long-term.

I’d be inclined to recommend the three-loss plays if I had to choose one, just due to the extra number of plays.

### Good Teams After Loss

You can do a lot worse than to focus on good teams after a bad performance since those teams will usually come out with a chip on their shoulder in their next game. Over the course of a long season NBA teams will have some games where they just go through the motions or don’t exert as much energy as they possible could. A good team who has been thumped in their previous game typically isn’t one of them and getting an honest effort is all you ask for from an NBA team.

For our purposes here we’ll classify a team that has a winning percentage of 66% or better after Jan. 1 a good team. The January qualifier is put in there to ensure that teams have enough games in the books to truly be classified as a good team. A team who starts out the year 4-2 may or not be good when the season ends, while a team who is 22-11 is more likely to have a strong season.

Since the start of the 2015-16 season, these teams are 159-125-7 (56%) after a loss and 133-102-4 (56.6%) as favorites after a loss, so they’ve been decent plays.

Between the three systems you can expect close to 70 to 90 plays per season, depending on the parameters you select, such as away favorites on a three- or four-game losing streak or if you play all good teams off a loss or just those who are favorites.

Using the three situational plays above and using them in their best-performing situations, meaning away favorites off at least three losses and good teams as favorites off a loss, would have produced the following records for each of the last five seasons:

2015-16: 44-33

2016-17: 49-44-2

2017-18: 47-40

2018-19: 54-28

2019-20: 45-30-3

Total: 239-175-5 (57.7%)

Aside from the 2016-17 season, which was essentially break-even with a net profit of .6 units, a combination of the three methods described has been solid over the years.

### Playoffs

I’ve never been a big fan of betting regular amounts in the playoffs, as lines tend to be a little sharper, but realize many people do, so I’ll include some playoff material here. Any talk of betting the NBA playoffs should probably start with the “Zig-Zag System,” although it’s no longer a profitable situation.

The Zig-Zag method was a decent system for a number of years but has become too well-known among bettors and sportsbooks. It simply involves betting on the team that lost straight-up the previous game of a playoff series. But in the last three seasons, the method has gone just 88-109-1 (44.7%), which includes a 31-25-1 mark in the 2018-19 postseason.

The method did have its best success back in the 1980s and 1990s and is one of those things most bettors have heard of but has been pretty much a 50-50 proposition over the past 15 years. Teams who lost two straight games have shown a slight profit over the years, but were just 12-15 ATS in the 2018-19 playoffs, so it’s best to just stay clear. I wouldn’t let the fact that a team lost the last game necessarily keep you off that team if your handicapping shows it’s a decent wager, but it also shouldn’t be the lone reason you make a play, either.

### Home Underdogs in the Playoffs

One trend that we’ve seen in the past few seasons is the dismal effort of home underdogs, as since the 2015-16 season through the 2018-19 playoffs, home dogs are just 27-51-1 against the spread, with much of that due to the pathetic 3-19 ATS record in the 2016-17 playoffs. Teams are still just 24-32-1 in the other seasons, but it’s one of those trends that probably looks a little better than it actually is due to the one season.

After home underdogs went 12-12 in the 2015-16 playoffs, they’ve posted records of 7-12 in 2017-18 and 5-8-1 in the 2018-19 postseason, so they have had three straight losing years. But with the 2019-20 playoffs being played a neutral site, it remains to be seen how this is going to play itself out.

With home underdogs performing so poorly, it’s no surprise that away favorites have done well, with away favorites off a win going 39-20-1 and away favorites off a loss going 12-7. Away favorites off a loss as a favorite were 11-5.

### Lower Scoring Games

It’s no secret that NBA teams put forth a little more effort on the defensive side of things in the postseason, but the oddsmakers and your fellow sports bettors know that as well. From the 2015-16 NBA season through the 2018-19 season there was an average score of 106.3 points per game in the regular season and 104.4 points in the playoffs, so expect roughly four fewer points per game in the postseason. The average total in the regular season was 212.3 compared to 210.1 in the playoffs, so while the totals are adjusted downward slightly, it’s not quite enough to match the scoring average drop. So it’s no surprise that unders are basically a break-even proposition at 153-169-7. If teams have played two or more straight overs, the record is 21-28-1.

The 2016-17 NBA playoffs were a bit of an aberration, as postseason scoring was actually slightly higher than it was during the regular season. As a result, playoff totals were 48-30-1, which is the main reason totals are relatively close to 50-50 over the past few seasons. Following the 2016-17 season, we’ve seen marks of 36-44-2 and 37-43-2 in postseason totals.

I’ve been telling sports bettors for more than 15 years to treat the playoffs the same as any other game and sports bettors have been ignoring that advice for the past 15 years, as well. If you absolutely have to bet on the playoffs, I’d run the numbers for the two statistical methods and subtract several points from each team to account for the lower scoring averages that we’ve seen in the playoffs.

If nothing develops out of that and you still want to place a wager on a game, I’d most likely lean to the unders, but make sure it’s just a token wager or “action wager” as I call them. I’ve seen too many above-average handicappers hurt themselves in the long run by insisting on having bets on the all of the big televised games. Turning a 2-1 day into a 3-3 day because you wanted to have something riding on the games isn’t good for your bankroll.