Sunday, March 1, 2015

The Trailblazers have had the most heartbreaking losses this season (on average)

Portland Oregon - White Stag sign
Choking away victory is
alive in Portland
I have a new NBA feature on the site: Team Season Profiles. This page aggregates win probability information at the season and team level. My previous post covered the four factors numbers on the right side of the table. This post will cover the columns on the left labelled "Game Profile".

The first column, "EI", is the average Excitement Index for each team's games. This is a "live" version of the table I shared in my recent FiveThirtyEight article on "exciting" NBA games. The Excitement Index is measured at a game level, and represents how much the win probability graph "travelled" over the course of the game. The Phoenix Suns still lead the league in excitement, with an average index of 6.78, but the Lakers have now overtaken the Spurs at the number two spot.

There are also two columns called "wCB" and "lCB". This is the average comeback factor for each team, split by games won versus games lost. The comeback factor is the winning team's odds of winning at their lowest point in the game. The higher the comeback factor, the bigger the comeback.

When sorted by losing comeback factor (lCB), the Portland Trailblazers "lead" the league, meaning the games they lost have a high average comeback factor, such as allowing the Mavericks to comeback from 830-1 odds last month. You could also consider this a "choke" index. Mathematical note: to minimize the effect of outliers, I take the geometric mean of the comeback factor, rather than a standard arithmetic mean.

One of the nice side effects of the NBA's data explosion is that no matter how awful your team is, you're bound to find something at which they excel. The Philadelphia 76ers don't win too often, but when they do, it's dramatic, as they have the highest average comeback factor for their wins (all 13 of 'em).

Explaining Team Win-Loss Records

A useful, sometimes ignored distinction in sports analytics is the difference between a "narrative" stat and a "predictive" stat. Narrative stats tell you what happened. Predictive stats tell you what will happen. Predictive stats are what your general managers, bookies, and gamblers care about, but it's usually the narrative stats that get the most attention. In addition, narrative stats are often mistaken for their predictive cousins (it happens with elections too).

Win probability added is, perhaps, the ultimate narrative stat. It can distill any play down to its impact on what we care about the most: winning. In last week's post on how NBA games are won, I used my win probability model to assess the relative importance of the "four factors" of basketball success, leading to the ground breaking conclusion that shooting is very, very important to the game of basketball (take that, Chuck).

In this post, I introduce a new feature to the site: NBA Team Profiles. In the same way I can use my narrative win probability added stat to take stock of the four factors, I can do the same for team win-loss records. The new feature breaks down a team's win loss record according to the four factors, and further split by offense and defense. Here is an example:

Wednesday, February 18, 2015

New NBA Features at FiveThirtyEight

Today, FiveThirtyEight is running two new features based on my NBA win probability model. The first is a Datalab article on "exciting" NBA games this season and whether those are predictable in advance.

The second is an interactive that summarizes each team's average win probability by minute for the 2014-15 NBA season (pre-All Star break). In effect, it's a real time evolution of a team's win-loss record. For example, midway through the first quarter, the Sacramento Kings still looked like an above 0.500 team. One of the nice things about working with FiveThirtyEight, aside from the added exposure it brings here, is the opportunity to work with their talented data visualization experts - Allison McCann for this feature, and Reuben Fischer-Baum for the NFL Playoff Implications weekly interactive.

Monday, February 16, 2015

How NBA Games Are Won

Basketball netWhat's more important in basketball: rebounding or getting to the foul line? Field goal percentage or forcing turnovers? These questions aren't new, but for this post I will use my win probability model to provide a new perspective on what matters most when it comes to winning basketball games.

Dean Oliver, pioneer of basketball statistical analysis, identified what he termed the "four factors" of basketball success in his influential book Basketball on Paper. Those four factors are:
  • Shooting
  • Free Throws
  • Rebounding
  • Turnovers
Nearly everything that is important to the game of basketball can be attributed to one of those four factors. But is each factor created equally? Or is one "more equal" than the others? Oliver himself tackled this question, using his futuristically-titled RoboScout program. Here is how Oliver assessed the relative importance the the four factors:

Saturday, February 7, 2015

Anthony Davis's Torrid MVP Pace

Anthony Davis is still considered a long shot to win NBA MVP this year (despite recent heroics). But by at least one measure (and others), it's not even close - Anthony Davis is the league's MVP. Using my win probability model, I can assign a value to each player's contributions, based on how those contributions affected their team's chances of winning. This approach automatically devalues garbage time stats, and assigns more credit for clutch performance. When ranked by Win Probability Added (WPA), Davis' total of 7.32 is far and away the league's best this season. Atlanta's Kyle Korver is a distant second with 4.86. Here is the top 10:


Friday, February 6, 2015

Updated NBA Win Probability Calculator

The odds of winning a game when down by 6
with 18 seconds left are approximately 250 to 1.
Last month, I rolled out a new version of my NBA Win Probability Graphs and Box Scores (new link | old link). In addition to adding some new features, such as the option of displaying real time along the horizontal axis, the underlying win probability model was rebuilt as well. The dataset was updated and expanded, model parameters were further optimized, and handling of late game situations was improved, particularly in the final seconds.

Until now, that new model was only used to generate the graphs. The interactive Win Probability Calculator was still using the old model. The calculator tool has now been updated with the new, improved model. I have also removed the "Beta" tag that had been there since its inception.

But how do I know the model is improved, and not just new? One way to assess a probability model's accuracy is by measuring log-likelihood. Likelihood, in this context, signifies the probability the model assigned to any specific game outcome. For example, if the model says that the win probability for a team is 15%, and the team actually goes on to win the game, the likelihood is 0.15. If the team lost, the likelihood was 85%. We can do this calculation for all game situations in which the model estimates a win probability. The total likelihood is just the product of all of those individual likelihoods. As a mathematical convenience, one often takes the natural logarithm of that product.

Sunday, February 1, 2015

Squares Probabilities at FiveThirtyEight

In a squares pool today? Want to know if your square is any good? Check my latest article at FiveThirtyEight: How Much Money You're Going to Win Playing Super Bowl Squares.

Expected payouts are shown for the standard version of Super Bowl squares, in which payouts occur based on the score at the end of each quarter. I also calculated expected payouts for the "Every Score Pays" rules, in which 5% of the pool is dished out for every score change (including extra points).

In doing some idle googling for the article, I came across the following on Leon Lett and the hate mail he received from squares owners after Super Bowl XXVII (didn't make the edit, so I thought I'd share it here):