The Power Rank uses data and analytics to make accurate predictions for football and March Madness. I developed these methods based on my PhD in applied math from Stanford.
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If you sign up for my free email newsletter, you’ll get my March Madness cheat sheet that makes it drop dead easy to fill out your bracket. These predictions are based on my team rankings in which the higher ranked team has won 71.3% of games (658-265) since the 2005 tournament.
During football season, you get a sample of my best football predictions usually saved for paying members of the site. Each week, I also provide analysis on each of the games.
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Major League Baseball
The matchup shows the projected FIP for each starting pitcher according to ZiPS.
The integer in parentheses gives the team’s rank in The Power Rank.
Since Apr 01, 2021, the team with the higher win probability has won 104 of 197 games for a win percentage of 52.8%.
The team favored by the markets has won 100.0 of 197 games for a win percentage of 50.8%.
Games on Sunday, April 18, 2021.
St. Louis (John Gant, 4.20) at Philadelphia (Aaron Nola, 3.55).
Philadelphia (19) has a 55.0 chance to beat St. Louis (18).
Arizona (Madison Bumgarner, 5.07) at Washington (Stephen Strasburg, 3.73).
Washington (11) has a 65.7 chance to beat Arizona (22).
San Francisco (Alex Wood, 4.11) at Miami (Pablo Lopez, 4.17).
San Francisco (20) has a 53.2 chance to beat Miami (25).
Pittsburgh (Chad Kuhl, 5.07) at Milwaukee (Freddy Peralta, 4.04).
Milwaukee (15) has a 70.2 chance to beat Pittsburgh (30).
New York Mets (Marcus Stroman, 4.16) at Colorado (Antonio Senzatela, 5.28).
New York Mets (4) has a 73.2 chance to beat Colorado (29).
Los Angeles Dodgers (Trevor Bauer, 3.52) at San Diego (Blake Snell, 3.64).
Los Angeles Dodgers (1) has a 56.1 chance to beat San Diego (2).
Atlanta (Bryse Wilson, 4.42) at Chicago Cubs (Kyle Hendricks, 4.25).
Atlanta (8) has a 55.2 chance to beat Chicago Cubs (21).
Tampa Bay (Andrew Kittredge, 3.76) at New York Yankees (Gerrit Cole, 3.05).
New York Yankees (3) has a 62.1 chance to beat Tampa Bay (12).
Chicago White Sox (Dallas Keuchel, 4.35) at Boston (Tanner Houck, 4.99).
Chicago White Sox (6) has a 58.7 chance to beat Boston (14).
Toronto (Robbie Ray, 4.27) at Kansas City (Brady Singer, 4.60).
Toronto (9) has a 59.2 chance to beat Kansas City (23).
Baltimore (John Means, 4.88) at Texas (Kyle Gibson, 4.52).
Texas (26) has a 55.5 chance to beat Baltimore (28).
Minnesota (J.A. Happ, 4.41) at Los Angeles Angels (Alex Cobb, 5.00).
Minnesota (7) has a 55.9 chance to beat Los Angeles Angels (10).
Detroit (Matthew Boyd, 4.17) at Oakland (Chris Bassitt, 4.67).
Oakland (16) has a 55.7 chance to beat Detroit (27).
Houston (Jake Odorizzi, 4.66) at Seattle (Nick Margevicius, 4.71).
Houston (5) has a 59.8 chance to beat Seattle (24).
Cleveland (Shane Bieber, 3.17) at Cincinnati (Wade Miley, 4.72).
Cleveland (13) has a 62.2 chance to beat Cincinnati (17).
Using replacement level fip for
Chicago White Sox (, 5.00) at Boston (Martin Perez, 4.78).
Chicago White Sox (6) has a 52.5 chance to beat Boston (14).
These predictions are based on my college basketball team rankings. The higher ranked team by this model has won 71.3% of tournament games since the 2005 tournament.
Members of The Power Rank have access to more accurate predictions. To learn more, click here.
1. Baylor versus Gonzaga at a neutral site.
Gonzaga (1) will beat Baylor (2) by 4.5 at a neutral site. Baylor has a 36% chance of beating Gonzaga.
These predictions are based on my team rankings that take margin of victory in games and adjusts for strength of schedule. There is also a preseason component that gets less weight with each week.
In 2020, the college football team rankings are based on games from the 2019 and 2020 season, with the current season getting twice the weight. A home field of 1.2 points is used.
European Club Soccer
These predictions are based on expected goals (xG) from past matches. This raw data is obtained from FBRef. I adjust for strength of schedule based on a least squares algorithm, which is equivalent to the Simple Rating System.
After these schedule adjustments, I have offensive and defensive ratings for each team. These numbers imply goal rates for each team in a match.
I assume a Poisson model and calculate the probability for a win, loss and draw. I’m assuming a home advantage of 0.12 goals based on matches with no fans.
To learn more about how the efficiency prediction works, check out my ultimate guide to predictive college basketball analytics.