WNBA Power Ratings

Here are my WNBA power ratings through games of June 12. They’re not quite as sharp as they typically would be at this point of the season due to having hybrid numbers to start the year on the basis of all last year’s games being played at a neutral court. They should get a little stronger as the season goes on. Las Vegas has the best home rating and Seattle has the best road rating. Numbers can change after each game.

You can quickly convert to a single-number power rating by subtracting a team’s defensive rating from its offensive rating. A team that is 88-3 will be an 85, while a team that is 88 – (-3) will become a 91.

To use the ratings, take the home team’s offense and add the road team’s defensive number and do the same for the road team, by taking its offensive number and add the home team’s defensive number.

TeamHome RatingAway Rating
Atlanta83 - 382 - 5
Chicago85 - 082 - (-4)
Connecticut83 - (-7)80 - (-10)
Dallas84 - 385 - 2
Indiana 82 - 478 - 9
Los Angeles83 - (-6)78 - (-4)
Las Vegas89 - (-5)88 - (-3)
Minnesota86 - (-5)85 - (-3)
New York81 - 078 - (-1)
Phoenix85 - (-2)88 - (-3)
Seattle87 - (-3)88 - (-6)
Washington84 - (-4)81 - (-3)



2021 WNBA Season

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.

TeamsYards Per PlayYards Per Point
Colts -1Colts by 1
TitansTitans by 5.5
Detroit -4.5Lions by 1.5Lions by 5
Cleveland -3Cleveland by 2Cleveland by 2
Packers -13GB by 8GB by 7.5
Eagles -3Eagles by 2
GiantsGiants by 3
Tampa Bay -6Tampa by 7
CarolinaCarolina by 2
Las Vegas -4Raiders by 2Raiders by 4.5
Cardinals -2.5Arizona by 4.5Arizona by 4.5
ChargersChargers by 1.5
Miami -1.5Miami by 20
Pitt No linePitt by 9.5Pitt by 8.5
SeattleSeattle by 5
Rams -1.5Rams by 5
San Fran
Saints -9.5New Orleans by 3NO by 2
Baltimore -7.5Balt by 14Raven by 2.5
Vikings -2.5
BearsBears by 2.5Bears 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.