How to quickly estimate the impact of players during a basketball game?
By: Guillaume Blot
Tags: NBA, Sport Analytics, Basketball, Data Visualization, Statistics

An Overall Basketball Player Impact Visualization.

Although graphical innovations have enriched access to sports statistics recently, not all of them seem to meet the now well-established specifications of Data Visualization. Major sports insiders are still using traditional table charts displaying a bunch of raw statistical lines for players and teams.

Besides that, some analysts are trying to imagine simpler ways to illustrate advanced and basic statistical data collected during the games. This is the case in Viziball, a free basketball game analysis website based on Data Visualization techniques and dedicated to a large community of fans, gamblers, journalists, basketball professionals, and even data science enthusiasts.

I would like to introduce you to some features that these techniques provide.

In this article, I am focusing on the Game Overview chart from the post-game page. This chart brings several pieces of information regarding how players have performed during the game. With a single chart, we can quickly estimate the overall impact of each player depending on the time he spent on the floor. This is done using several statistics: minutes played, +/- and PIE (Player Impact Estimation).

The PIE shows the overall contribution to the game for a specific player. This presents the contribution of a player as a percentage. Adding the PIEs of all players from both teams gives 100%. Each time a player contributes to a positive game event (point, rebound, assist, block, interception), it will lower everyone else’s PIE score. Though an identical stat line on two different games won’t necessarily give the same measure of impact, and conversely, two identical measures would not necessarily come from identical statistical lines.

Knowing this is not even essential to get relevant insights from the chart. All we have to know to start with is that bubbles represent players and that these bubbles have different sizes depending on the impact value (PIE) and are based according to the time each player has spent on the floor (horizontal) and +/- (vertical). On this basis, it is pretty simple to compare all players together.

Who had a significant impact on the game? Which bench players had a great night? Which starters had a poor impact? How is the impact shared among the team? Does a player polarize the game? All comparisons might be possible, and all these interrogations can be translated in an easier way. Who has the larger circle? Who has the top-based circle? Which players from the left have big circles? And reciprocally, which players from the right have small circles? Are circle sizes equivalent? Is there a circle significantly bigger than others?

For example, we can observe a post-game analysis from game 6 of the 2020 NBA finals. Very easily, we can see that two players from the Lakers (LeBron James and Anthony Davis) are bigger and higher than the rest of the team, which is mostly represented by small and low circles. This is also the case with the Miami Heat with one big red circle (Jimmy Butler), some medium-sized circles (Duncan Robinson and Kendrick Nunn), and a few smaller ones. We can also quickly view that players with significant impact are not those who played the most and reciprocally. This is the case for dimension (Jae Crowder is second on Miami roster in minutes played and has the smallest circle of the team), but also positions (Danny Green with few minutes but high +/-, which means that while he was on the field, his team had a positive point differential).

Players impact overview during game 6 of 2020 NBA finals

The layout algorithm avoids circles overlapping using force-directed repulsion. Big nodes push smaller ones. That’s the reason why circles are not exactly positioned where they are supposed to be. Visual analysis doesn’t need to be exact down to the last comma. It has to be understandable.

This gives a quick and easy picture of the game, where more precise information is available with a click. For example, scrolling over a player, one can read his exact PIE and +/- values.

And like straight numbers, these visual observations have to be taken as insights and not as real facts. Traditional limits dealing with statistics are logically inherited from the measures on which this chart is based. PIE formula aggregates basic stats like points, rebounds, assists, steals, and blocks, but also missed shots or personal fouls. If a player has an impact beyond this, it will not be recorded. And as everyone knows, basketball is not just filling the board. Screens, deflections, active hands, ball control, shot contesting, dribbling skills, cross over, aggressiveness, ball movement, rhythm, mentoring, or even speeches: those are important elements that are not considered by PIE measure.

Once, a professional head coach from a french franchise told me that he has a player who always ends the game with a poor PIE. But every time he removes him from the roster, he has lost the game. Indeed, we have to find ways to identify those types of players. Maybe we have to start thinking about how we can enrich our measurement by integrating those invisible stats. But coming with a new formula, we will still have to collect new data.

For now, we have decided to pair PIE with plus/minus. This helps in highlighting players with invisible impact, although it is still limited by the fact that players enter the game at various moments (starting lineup, garbage, money time) and that the impact of players extracted from the +/- stat is drawn among other players’ impact.

To go further, Viziball experiments with new techniques on each post-game analysis page: point differential evolution, player impact dynamics, or also impact on specific aspects of the game. Overall impact is just the starting point.

I wish to bring more details in a future article about how the rest of the game analysis works, but everyone can experience it right now on Viziball for more than 30,000 basketball games.