Monday, May 26, 2014

French Open Win Probability Graphs

As I did with the Australian Open (and intend to do with all grand slam events this year), I will be providing win probability graphs for all French Open matches. Here is the link: Tennis In-Game Win Probability.

My initial post on this topic, focusing on Victoria Duval's shocking upset of Samantha Stosur in the US Open has some additional background. For these graphs, each game starts at 50/50 because I am using generic probabilities for winning a point on serve and return (62.5% and 37.5% respectively). It is possible to alter the probabilities to line up with the pre-match odds, but it's not something I can automate very easily.

The Excitement Index and Comeback Factor concepts I use for the NBA also carry over to tennis matches. The most exciting game of the first round of the French Open was a marathon match between Facundo Bagnis and Julien Benneteau, featuring a fifth set that took 34 games to complete (Bagnis won 18-16). Here is the graph:

The biggest comeback comes from the women's first round, with Stephanie Voegele coming back from a 4.2% win probability to defeat Anna-Lena Friedsam.

Graphs will update daily(ish) throughout the tournament.

Sunday, May 25, 2014

Top Game Finder for the NBA

Top. Games.
As I mentioned yesterday, win probability graphs are now available for the 2012-13 NBA season. For the lazy among you that don't want to cycle through all 2500+ games one by one, I have created a tool that helps you find the top games of the past two seasons: the Top Game Finder (as with far too many features on this site, I got the idea from Advanced Football Analytics).

Excitement, Comebacks, and MVP's

The top dropdown menu offers three options for sorting games:
  • Excitement: This option will sort games by top value of the excitement index. The excitement index measures how far the win probability graph "travels" over the course of the game.
  • Comeback: This option will sort games by the comeback factor, which is the winning team's odds at their lowest point in the game. My win probability estimates only go down to the 4th decimal place, so any winning teams with a win probability of < 0.0001 show up with a comeback factor of "9999+".
  • MVP Performance: Win probability added (WPA) can be apportioned at the player level (see here for more information). I designate the player with the highest WPA in a game as the MVP. This option will sort games by the top MVP performance in terms of total WPA.
There are a variety of filtering options: by season, by team, by regular season/playoffs (or both), and by date.

A Sampling of Top Games

Sunday, May 18, 2014

Lebron has been ridiculously good in clutch situations these playoffs

In which I use advanced analytics and complicated modeling to support the provocative notion that Lebron James is good at basketball.

In this previous post, I laid out an approach for defining clutch play in the NBA, using my win probability model to "score" the impact of each play in a game. A clutch situation is a time of heightened sensitivity, where good plays are rewarded more, and mistakes punished more severely.

My current definition of clutch play is rather broad, including all plays whose impact exceeds the median value (e.g. a three pointer typically adds 4% in win probability, so a three pointer with a win probability added of 4.5% would be considered clutch). For this post, I will use a more restrictive definition of clutch situations, one that hopefully is more in line with general understanding. Instead of taking all plays above the typical value, I will only count plays that are above the 75th percentile.

Monday, May 12, 2014

Should the Thunder have fouled?

Scott Brooks and Maurice Cheeks
Scott Brooks and Maurice Cheeks
If my Twitter feed is any indication, the following are universally held opinions:
  1. The Thunder's Scott Brooks is a bad coach
  2. Not fouling the Clippers with 27 seconds left and trailing by 2 was a bad decision and evidence of 1. above
In this post I will use my win probability model to evaluate whether the Thunder would have been better off fouling the Clippers instead of playing for the stop (recap and win probability graph for the game in question).

Game State

After Westbrook's layup, the Thunder were down by 2, and the Clippers with possession. To evaluate this decision, I will set a low bar for Scott Brooks to clear. Let's assume that the Clippers held the ball for the full 24 seconds and allowed a shot clock violation. How does the win probability of a team down by 2 with possession and three seconds compare to the win probability of a team down by 2 with ~twenty seven seconds left and their opponent on the line with two free throws to shoot?

According to my win probability model, having the ball down by 2 with three seconds to go has a win probability of 9.1%. What is the corresponding "deliberate foul" win probability?

Sunday, May 4, 2014

Round 1 in Review

With the Spurs' anticlimactic victory over the Mavs, the first round of the NBA playoffs has finally come to an end. 50 games, 5 game 7's, and an average excitement index of 6.6 (compared to a regular season average of 5.9). Here is the first round in review:

Dame the first round MVP by just about any measure

By a significant margin, Damian Lillard is the MVP of the first round, with +1.94 Win Probability Added (WPA), a good +0.7 better than his next closest competition, Vince Carter. Lillard also leads in expected WPA, which is like WPA, but stripped of game context. It's effectively a measure of the quality of a player's box score stats. Lillard was also the top player in clutch WPA (clWPA), which measures how much excess WPA a player added by performing in clucth situations. See my post on Measuring Clutch Play in the NBA for more background.

As if that wasn't enough, Lillard also leads in my "kitchen sink WPA" stat, which adds up WPA contributions for all box score stats (i.e. standard WPA + blocks + steals + assists + rebounds). Lillard's +4.09 kWPA narrowly beats out teammate Nicolas Batum's +4.05 total.