THE POWER RANK

Tigers, Indians in dead heat for AL Central in 2015

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Screen Shot 2015-03-25 at 11.32.18 AMWho will win the AL Central in 2015?

To answer this question, I aggregated win total predictions in my first article for the Detroit News. I’ll bring a numbers based perspective on the Tigers all season (every other week early in the season, once a month when college football picks up in August).

The model combines the markets with 6 different predictions based on analytics. The ensemble predicts a dead heat between the Tigers and Indians atop the AL Central, as their win totals differ by less than half a run.

I’ll say more about this ensemble model in a few weeks, but here are the results for all 30 MLB teams.

1. Washington, 93.4.
2. Los Angeles Dodgers, 92.7.
3. St. Louis, 88.5.
4. Los Angeles Angels, 87.6.
5. Seattle, 87.2.
6. Boston, 86.2.
7. Pittsburgh, 84.2.
8. Cleveland, 83.9.
9. Detroit, 83.6.
10. Chicago Cubs, 83.4.
11. San Diego, 83.3.
12. San Francisco, 83.2.
13. Oakland, 82.9.
14. Toronto, 82.3.
15. Baltimore, 81.9.
16. New York Yankees, 81.3.
17. New York Mets, 81.2.
18. Tampa Bay, 80.7.
19. Miami, 80.7.
20. Kansas City, 80.3.
21. Chicago White Sox, 79.5.
22. Milwaukee, 78.8.
23. Cincinnati, 77.2.
24. Houston, 75.9.
25. Texas, 75.8.
26. Colorado, 72.5.
27. Atlanta, 72.3.
28. Minnesota, 72.3.
29. Arizona, 72.0.
30. Philadelphia, 68.5.

World Series win probability for 2014

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world_series_winprobAt the end of the regular season, I ranked baseball teams based on expected runs scored and allowed. The formula comes from Dave Smyth’s Base Runs, which on average estimates a team’s season run total within 1%.

The first number after a team gives the differential in expected runs, followed by offense and defense in parentheses. The record comes from baseball’s Pythagorean theorem with an exponent 1.83.

1. Los Angeles Angels, 131.99. (728.60, 596.61). Record: 95-67.
2. Washington, 125.88. (695.92, 570.04). Record: 95-67.
3. Oakland, 117.29. (684.77, 567.49). Record: 94-68.
4. Los Angeles Dodgers, 115.87. (737.74, 621.87). Record: 93-69.
5. Pittsburgh, 91.53. (724.80, 633.26). Record: 90-71.
6. Baltimore, 72.00. (718.53, 646.54). Record: 88-74.
7. Detroit, 59.78. (771.46, 711.68). Record: 86-76.
8. San Francisco, 52.06. (657.84, 605.78). Record: 86-75.
9. Tampa Bay, 35.93. (642.20, 606.27). Record: 85-77.
10. Seattle, 31.37. (606.33, 574.97). Record: 84-78.
11. Toronto, 28.01. (735.04, 707.04). Record: 83-79.
12. Cleveland, 22.12. (686.96, 664.84). Record: 82-79.
13. St. Louis, 16.59. (628.22, 611.63). Record: 82-80.
14. Kansas City, -4.46. (634.16, 638.61). Record: 79-82.
15. Milwaukee, -5.71. (659.09, 664.80). Record: 80-82.
16. New York Yankees, -20.64. (638.51, 659.14). Record: 78-84.
17. Chicago Cubs, -25.19. (634.64, 659.83). Record: 77-84.
18. Atlanta, -28.47. (602.24, 630.71). Record: 77-85.
19. Miami, -29.88. (662.45, 692.33). Record: 77-85.
20. Colorado, -34.93. (784.56, 819.49). Record: 77-84.
21. New York Mets, -46.29. (620.00, 666.29). Record: 75-87.
22. Houston, -49.93. (648.16, 698.10). Record: 75-87.
23. San Diego, -55.98. (539.81, 595.78). Record: 73-89.
24. Cincinnati, -58.94. (582.96, 641.90). Record: 73-89.
25. Chicago White Sox, -69.15. (670.81, 739.96). Record: 73-89.
26. Minnesota, -71.83. (700.10, 771.92). Record: 73-89.
27. Philadelphia, -72.87. (611.56, 684.42). Record: 72-90.
28. Boston, -80.61. (647.36, 727.97). Record: 72-90.
29. Arizona, -111.52. (620.73, 732.25). Record: 68-94.
30. Texas, -150.93. (629.36, 780.30). Record: 65-97.

Kansas City had a negative run differential by expected runs. They should have lost more games than they won during the regular season. Now the Royals are playing in the World Series.

San Francisco had the 4th best run differential among 5 NL playoff teams. They beat that 5th team (St. Louis) to return to the World Series.

Playoff baseball is crazy.

World Series Prediction

My numbers gives San Francisco a 53% chance to beat Kansas City in the World Series. The markets favorite a Kansas City team that has not yet lost a game in the playoffs.

Kansas City has home field because the AL won the All-Star game. If San Francisco had home field, their win probability goes up to 55%. I thought the gap would be bigger, but the 0.3 runs I use for home field advantage has a small effect on series odds.

The series win probabilities start with my MLB team rankings, which take raw run differential and adjust for strength of schedule. Also, I adjust for cluster luck based on the regular season.

In addition, the projections consider starting pitching through xFIP, an ERA type statistics that captures the skill of a pitcher through strike outs, walks and fly ball rate.

Daily predictions for each game appear on the predictions page.

Championship series win probabilities for the 2014 MLB playoffs

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boy_hitting_foreheadIt’s an embarrassment, but I’m not going to hide from it. All the favored teams by my numbers (and the markets) lost in the division series.

And it’s not like the favored teams lost in a game 7 coin flip. The favored teams won two games (Dodgers over Cardinals, Nationals over Giants).

As Billy Beane said about his analytics in Moneyball, “my shit doesn’t work in the playoffs.”

Someone tweeted that quote at me before the division series started. I blew it off. If you’re betting on heads, you want the coin to come up heads 52% instead of 50% of the time. Always.

But Billy’s words have new meaning after the division series. It’s not that analytics don’t work in the playoffs. It’s that we should appreciate the randomness of a short series.

Let’s also remember that the team that wins the World Series might play more playoff games than a college football team does all season.

Here are numbers for the championship series.

  • San Francisco has a 52.6% chance to beat St. Louis. No, there is Cardinal Devil Magic in this prediction.
  • Baltimore has a 56.6% chance to beat Kansas City.

These win probabilities start with my MLB team rankings, which take run differential and adjust for strength of schedule. Also, for the first time, I adjust for cluster luck based on the regular season.

In addition, the projections consider starting pitching through xFIP, an ERA type statistics that captures the skill of a pitcher through strike outs, walks and fly ball rate.

Daily predictions for each game appear on the predictions page.

As of noon Eastern on October 10th, the markets give both San Francisco and Baltimore an implied odds of 55.1%.

Division series win probabilities for the 2014 MLB playoffs

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My team rankings and adjustments for starting pitcher give these numbers.

  • Washington 61.1% over San Francisco
  • Los Angeles Dodgers 73.3% over St. Louis
  • Los Angeles Angels 66.2% over Kansas City
  • Detroit 59.7% over Baltimore

These win probabilities start with my MLB team rankings, which take run differential and adjust for strength of schedule. Also, for the first time, I adjust for cluster luck based on the regular season.

In addition, the projections consider starting pitching through xFIP, an ERA type statistics that captures the skill of a pitcher through strike outs, walks and fly ball rate.

Daily predictions for each game appear on the predictions page.

Assumptions behind the calculations

Michael Wacha is not projected to start for St. Louis, which leaves John Lackey and Shelby Miller to start game 3 and 4.

I’m assuming Gio Gonzalez and Yusmeiro Petit start for Washington and San Francisco respectively in game 4.

Note that Baltimore is the only team with home advantage that doesn’t have the higher odds to win the 5 games series.

How to use baseball analytics for a profitable sports investment

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true_oddsDo you bet on baseball? Are you looking for an extra edge based on data and analytics?

Onside Sports has new solution. While they launched as a social sports app last year, they have now developed True Odds, a data driven prediction system for baseball. True Odds, an in-app purchase, has a 298-271-10 record this season through September 9th.

I had the opportunity to talk with Kai Yu, the brains behind True Odds. While he obviously could not tell me everything about his methods, he did share quite a bit, which I’ll share in this post.

If you’re eager to get a free trial of their picks, click here and use the code THEPOWERRANK.

Baseball from its fundamental interaction

True Odds starts with the matchup between pitcher and batter. Based on historical data, it seeks to estimate probability of an event such as Miguel Cabrera’s hitting a home run off James Shields.

As part of this analysis, Kai had to carefully sort out which variables predict the future and which variables tend towards randomness. He noted contact rate as an import skill for a hitter. It’s tough to strike out Victor Martinez no matter who pitches to him.

This bottom up approach has advantages over the top down approach that looks at overall team performance. Often times, this top down approaches looks at a team’s runs scored and allowed. However, these numbers can be greatly affect by the sequencing of hits, or cluster luck. Combining pitcher batter matchups with the simulation method below does not have these problems.

Random simulations

Based on the probabilities from every pitcher batter matchup, True Odds uses a random simulation to play the game many times. Each simulation is different, and a set of simulations gives the probability that certain events happen, such as a Detroit win over San Francisco or a total of more than 7 runs for Oakland and Seattle.

To accurately simulate a game, True Odds must know both the pitcher and the opposing line up. This method naturally accounts for injuries.

Other quants have also used pitcher batter matchups and random simulations to profit on baseball. For example, check out this excellent Q&A with David Frohardt-Lane on Regressing, Deadspin’s sports data blog.

A multitude of other factors

Kai also stressed the importance of other factors, such as park, weather and umpires. True Odds incorporates these factors in predicting the outcomes of games.

Let’s discuss umpires, who can impact home field advantage. As Jon Wertheim and Tobias Moskovitz discussed in their book Scorecasting, umpires tend to call more strikes on road than home batters. This tendency increases in high leverage situations, such as two outs with the bases loaded in a close game in the bottom of the ninth.

However, umpires might not play as big a role in home advantage anymore. Through September 8th, home teams have scored a mere 49 more runs than road teams. This 0.02 runs per game is much lower than the historical average.

Major League Baseball might be keeping a more watchful eye on umpires with cameras. I bet True Odds has a grasp on this.

Does FIP apply to every pitcher?

The most interesting part of my conversation with Kai concerned whether fielding independent pitching applied to every pitcher.

To recap, fielding independent pitching comes from the research of Voros McCracken, who discovered that pitchers do not affect batting average on balls in play (BABIP). Pitchers have control over their strike outs, walks and home runs allowed. However, 30% of balls hit in play become hits, and deviations from this average for a pitcher strongly regress to the mean.

This research led to the development of FIP, a runs allowed statistic that only considers strike outs, walks and home runs. It should replace ERA in any discussion of pitching performance.

However, Kai suggested FIP doesn’t apply to all pitchers. He cited Seattle’s Chris Young as a pitcher who consistently has a lower ERA than FIP. This reminds me of an excellent analysis of Mark Buehrle and how his defense makes him a better pitcher than FIP suggests.

Try out True Odds for free

Onside Sports has done a remarkable job using data to find value in the baseball market. Their predictions have registered 298 wins, 271 losses and 10 pushes for a return on investment of 11% through September 9.

As a reader of The Power Rank, you can try out True Odds for free. Follow the steps under this video and use the code THEPOWERRANK. With only 4 weeks left before the baseball postseason, check it out today.