Saturday, January 24, 2015

Australian Open Win Probability Graphs

Win probability graphs for the Australian Open are now available. Unlike my win probability models for the NBA and soccer, which required regression analyses and lots of smoothing, the development of the tennis model was more straightforward, if a bit tedious. Once you specify the probability of winning a point on serve, the rest is no worse than a college-level probability exercise.

The graphs come in two versions. The "50/50" version assumes the two competitors to be of equal strength, with a 67.5 percent probability of winning a point on serve, and 32.5 percent probability of winning a point when returning. The "Market" version of the graphs adjusts both the serve and return probabilities up or down so that the starting probability aligns with the pre-match betting odds. For example, take Andreas Seppi's third round upset of Roger Federer. The betting market gave Seppi just a 6 percent chance of beating Federer, which implied a 67.2 percent serve probability for Federer and a corresponding 57.6 percent serve probability for Seppi. Here is the graph:

Friday, January 16, 2015

NBA Win Probability - Take Two

On the eve of the 2014-15 NBA season, I hinted at new features I planned on adding to my NBA Win Probability tools. I believe the word I used was "soon", and I suppose this qualifies, but it took a bit longer than expected. I'm not sure what went wrong, I even took into account Hofstadter's Law.

The new graphs and their corresponding features version use a different data source that goes back to the 1996-97 NBA season. I just have current season results up now, but I have plans to add prior seasons in the coming weeks, barring any data issues or website strain (I'm dying to see what Tracy McGrady's 13 points in 35 seconds looks like on a win probability graph).

With the new data source, I created a new win probability model, removing some of the kludge-iness of the old version. The new model appears to perform better out of sample, resulting in a higher log likelihood score for the 2014 season, compared to the existing version.

Saturday, January 10, 2015

College Basketball Vegas Rankings

Betting market rankings for College Basketball are now up and running and will update daily throughout the season. As with my NFL, NBA, and College Football rankings, these are an attempt to distill the wisdom of the betting market. I do this by reverse engineering an implied ranking from the market point spreads and over/unders (more background at my Methodology page).

The key metric is what is known as "Generic Points Favored", or GPF. It is what you would expect a team to be favored by against an average Division 1 opponent on a neutral court. With the help of the over/unders, a team's GPF can be decomposed into its offensive and defensive components: oGPF and dGPF. One callout is that these offensive and defensive numbers are not pace-adjusted - meaning that they represent total points scored or allowed per game. If a team tends to play at a quicker than average pace, it will tend to score more points and allow more points, thus inflating their oGPF and deflating their dGPF. For a more "pure" representation of offensive and defensive talent, you need to adjust stats to a per-possession basis, such as those published at kenpom.com.

Friday, January 2, 2015

Playoff Edition of NFL Rankings Now Available

NFL playoff treeA playoff version of my NFL rankings are now available and will be updated daily. The new table has columns showing every team's probability of advancing past each round of the playoffs. The rankings will also be updated each day with the latest point spreads and over/unders. When calculating the rankings, I exclude week 17 of the regular season as there are too many meaningless games that can skew the betting lines.

I also have a column on the right with each team's implied "fair" Superbowl odds. Comparing these odds to the futures odds being offered by 5Dimes, bets on the Green Bay Packers, Denver Broncos, and New England Patriots would be positive expected value. The futures market views Seattle as the clear Superbowl favorite, much moreso than what my rankings indicate.

My Superbowl probabilities actually align much closer with the playoff odds at Advanced Football Analytics.

Thursday, January 1, 2015

2014 Early Season Power Rankings - The Results

Bill Belichick 2012 Shankbone
Fashion icon Bill Belichick
This post is a follow up on this post from October in which I test the accuracy of various NFL power rankings - based on their ability to predict future win/loss records.

Like God and his beetles, the internet has an inordinate fondness for ordered lists. Case in point: NFL power rankings. Nearly every major (and minor) sports site has their own subjective assessment of the relative strength of each NFL team - NFL.com, CBS, ESPN, and SBNation just to name a few. There are also a variety of objective stat-based rankings from which to choose, from Advanced Football Analytics simple and open-source team efficiency rankings, to Football Outsiders' more complex and proprietary DVOA model.

Falling somewhat in between a subjective and objective approach are my betting market rankings. They are objective in that the recipe is fixed ahead of time, and requires no judgment on my part (I'm just turning the crank). But the inputs to the model are the Vegas point spreads, which are subject to the whims and prejudices of the market - and the bookies who do their best to keep their books balanced.

Saturday, December 27, 2014

Transitive College Football Power Rankings - Bowl Games Edition

Three paths from A to BIn a recent post (My Team's Proxy Can Beat Up Your Team's Proxy), I laid out an approach for comparing two college football teams (or two teams from any sport, really). The original version allowed the user to compare any two top 25 teams. For this post, I have created a Bowl Games version which automatically generates these comparisons for every Bowl Game matchup of the season. The basic idea is to generate comparisons by trying to connect teams via "paths". We find our paths by looking at prior game results.

For example, the Alabama Crimson Tide play the Ohio State Buckeyes on New Years Day in the first round of the College Football Playoffs. These two teams have not played each other this season, so there are no direct paths connecting them. In addition, they do not have any common opponents, so there are no paths of length two connecting them either. But some of their opponents have played each other, so there are paths of length three available:

Wednesday, December 17, 2014

Turnover Index - Week 16

Here are the Turnover Index picks for Week 16. The Turnover Index is a simple betting strategy based on the theory that the market overvalues defensive turnovers when judging team strength. See here and here for more background.

Week 14 results

Our week 14 bet was successful against the spread, with the Raiders covering against the Niners. Here are the season to date results:
  • Against the Spread: 8-2
  • Starting Bankroll: $1,000
  • Current Bankroll: $1,073 (8% ROI)
And here are the week by week results:

weekbetswonstarting
bankroll
amount betprofitsending
bankroll
7 2 2 $1,000 $51 (5.2%) $47 $1,047
8 2 1 $1,047 $26 (2.6%) $1 $1,048
10 2 2 $1,048 $32 (3.1%) $29 $1,077
12 2 1 $1,077 $26 (2.5%) ($11) $1,066
13 1 1 $1,066 $7 (0.7%) $6 $1,073
14 1 1 $1,073 $6 (0.6%) $5 $1,079