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Why Should We Use Viziball to Analyze and Compare Basketball Players’ Performance?

One look at a chart can help us draw conclusions. That's the beauty of data visualization.

In a previous article, I discussed how we use PIE (Player Impact Estimate) within Viziball, a basketball analytics website. This general measure proposes to aggregate statistical outputs in order to obtain a quick summary of how a player influences the game.

Despite all it gives us, this metric has limitations (as with any indicator). The first is that we only have visible game facts and that there are ways to influence a game other than producing stats. The second limitation is that the PIE measure gives a general assessment and that it is, therefore, difficult to trace the affected game aspects.

It’s this second limitation that we try to deal with. In order to provide more elaborate insights, we propose a segmentation of the PIE measure around 8 axes: shooting, scoring, offensive aggressiveness, defensive aggressiveness, go-to guy, catch & score, play-making, and clutchness.

For each of these axes, we calculate a score (between 0 and 100) based on various formulas that I will discuss next. But before I go any further, I would like to introduce our visual feature, which is the reason why we have to put all the axes on the same scale: the spider chart.

Damian Lillard's career performance overview. Chart by Viziball.
Damian Lillard’s career performance overview. Chart by Viziball.

Spider charts (or Radar charts) are useful for comparing data in an attractive way. They are very effective for seeing which variables are scoring high or low within a dataset, making them ideal for displaying performance statistics. Moreover, spider charts are easily understood by a large audience and are already quite widespread (which is not the case for all data visualization graphics). Then, the chart would almost go without comment and can be easily shared.

Analysts tell that this brings a lot of insights very quickly. How does a player control and distribute the ball (play-making indicator)? Where is he situated in the team hierarchy (go-to guy indicator)? Is he good during the money time (clutch indicator)? What is his offensive profile (offensive aggressiveness)? How does he impact the defense (defensive aggressiveness)? Is he creating his own buckets, or is he more of a catch & shooter (catch & score)?

To give you more details on how Viziball tries to provide some answers, here is how the indicators are established. Some are based on basic stats, while others are based on more advanced formulas or play-by-play analysis.

To give you more details on how Viziball tries to provide some answers, here is how the indicators are established. Some are based on basic stats, others are based on more advanced formulas or play-by-play analysis.

  1. Play-making: Based on the Assist to Turnover Ratio. This indicator measures ball control and the ability to create efficient plays.
  2. Shooting: Mixing Effective Field Goal Percentage (EFG%) and True Shooting Percentage (TS%) to measure the ability to score from any distance.
  3. Scoring: Number of Points.
  4. Go-to guy: Based on Minutes Played and Usage indicator (percentage of possessions ending in a player’s hands while on the field).
  5. Catch & Score: Ability to score following an assist (based on Assisted FG%). A low score doesn’t imply that the player misses his catch & shoots, but it means that most of his shots are coming from a personal action (no assist from a teammate).
  6. Offensive Aggressiveness: Drawn Fouls and Offensive Rebounds.
  7. Defensive Aggressiveness: Steals, Blocks, and Personal Fouls.
  8. Clutchness: this combines several basic statistics to produce a single value. Unlike PIE or Game Score, here we add a coefficient for each stat, depending on the remaining playing time and the point differential at the time the action occurred.

As you can see, all indicators are based on statistical outputs recorded during games. I do not give the normalization process that brings all indicators at the same scale. I agree that this process might sound arbitrary. But, what we have to keep in mind here is that we are trying to compare players’ performances. So at the end of the process, when all performances lay on the spider chart, that’s what we get.

It is also important to notice that no subjective opinions are introduced in our calculation. Like for example, saying that Rudy Gobert had a great night in defense after watching the game. Of course, it would have been comfortable to have a dashboard where we could configure and moderate values. Doing so, one can imagine many more axes. But this is not the philosophy of Viziball. We avoid introducing biases in the workflow. What we do is bring tools to analysts and insiders, not the opposite. Then they are free to draw conclusions.

To make one last comment, we can also say that this is not supposed to represent the intrinsic value of the player, like a video game could try to do. For any reason, the player’s performance could be altered by the context: choice of coaches, referees’ decisions, or even injuries.

Our positioning could be discussed for a long time (I also encourage you to send me feedback and help us enrich our model), but I think it’s time to see how we use the spider chart.

Post-game report

Every day and for all available games, Viziball establishes a post-game report in which each player’s spider chart is available.

For example, below is the spider chart of Bam Adebayo after his game against the Brooklyn Nets on 01/23/2021. Very quickly, and before knowing anything about his statistics during that game, we can understand that he had a great night in various aspects.

Bam Adebayo's performance overview on 01/23/2021. Post-game report by Viziball.
Bam Adebayo’s performance overview on 01/23/2021. Post-game report by Viziball.

At the same moment, Kyrie Irving had a clutch night: 3-pointers, driving layups, and key free throws to help lift the Nets in the 4th quarter. This can be read through his spider chart, where both Shooting and Clutch indicators are high.

Kyrie Irving's performance overview on 01/23/2021. Post-game report by Viziball.
Kyrie Irving’s performance overview on 01/23/2021. Post-game report by Viziball.

Viziball also provides a heat map in the same post-game report, showing the players’ impact per sequence. Here, this impact is represented by the intensity of the bricks. The darker it is, the higher the impact is high. Below, the 4 last bricks related to the last quarter look to be the most intense of Kyrie’s game. We can also obverse how this can be related to his points line (blue), which increases at the end of the game.

Progression of Kyrie Irving's performance during his game against Miami. Post-game report by Viziball.
Progression of Kyrie Irving’s performance during his game against Miami. Post-game report by Viziball.

Player’s profile page

Every player recorded in the Viziball database has his own player’s profile page, which has a spider chart among various other widgets. Using this feature, we can observe a player’s performance over a specific period, which can be their whole career, a particular season, or any personalized duration.

This web page can be highly customized (like showing or hiding the spider chart’s axes) and can be shared easily either using the personalized URL or the embedded mode.

At the bottom of the image is the date picker module. We can see the yellow line showing how general performance (PIE) evolves through the years.

Lebron James career performance overview. Chart by Viziball.
Lebron James career performance overview. Chart by Viziball.

This page also includes a timeline module called Career path, displaying the player’s career highest records and his first game with all the teams he played for.

Lebron James' career path. Timeline by Viziball.
Lebron James’ career path. Timeline by Viziball.

Comparing players

In my opinion, where the spider chart brings the most important insights is when we overlay several players. Viziball’s comparative feature makes it possible to visualize two players at the same time. This brings various scenarios: comparing opponents, comparing players from the same team to understand how they can fit together, or also comparing the same player at different moments of his career.

For example, below is a comparative spider chart with Jayson Tatum (green) and Bradley Beal (red) for the first few games of the current season (2020–2021). We can see their similarities on both catch & score, play-making and shooting indicators, but also some significant disparities on other aspects, with an advantage to Bradley Beal in scoring, and offensive & defensive aggressiveness.

On the other hand, Tatum seems to be brilliant in the 4th quarter during the selected period, as he gets an 84 clutchness indicator (vs. 49 for Bradley Beal).

Jayson Tatum (green) vs. Bradley Beal (red) performance over their first games of the 2020–2021 season. Spider chart by Viziball.
Jayson Tatum (green) vs. Bradley Beal (red) performance over their first games of the 2020–2021 season. Spider chart by Viziball.

Beal seems to be the first option in offense with a go-to guy indicator equal to 93 (vs. 78 for Jayson Tatum). To see how this correlates with traditional figures, let’s get a bit deeper. Beal records 35.6 minutes per game and a 37.8% Usage. Tatum has 34.3 minutes and 31.2% Usage.

Jayson Tatum & Bradley Beal average figures. Chart by Viziball.
Jayson Tatum & Bradley Beal average figures. Chart by Viziball.

This means that Jayson Tatum is sharing the ball a bit more. To go further, we can observe how usage is distributed among both teams. We can see below that Beal has a high usage compared with all his teammates (except Russell Westbrook, who has 31.42%), whereas the Celtics seem to share offensive phases among three go-to guys: Jayson Tatum, Jaylen Brown, and Kemba Walker, with 31.2%, 35.75%, and 32.35% Usage, respectively.

Usage distribution among the two teams : Wizards vs. Celtics. Chart by Viziball.
Usage distribution among the two teams : Wizards vs. Celtics. Chart by Viziball.

Observing performance evolution

Finding the future Most Improved Player (or also the Most Declining Players), distinguishing the evolution of a player when he changes teams, or just observing a player’s evolution on a particular aspect of the game: all this can be done thanks to this feature.

Personally, I chose to illustrate this feature with Nikola Jokic’s comparative spider chart:

  1. In blue are his stats from the 2019–2020 season (including the bubble and playoffs).
  2. In yellow are his stats from the first games of the current season.
Nikola Jokic's comparative spider chart over two periods : 2019–2020 (Dark blue) vs. first games of 2020–2021 (Yellow). Spider chart by Viziball.
Nikola Jokic’s comparative spider chart over two periods : 2019–2020 (Dark blue) vs. first games of 2020–2021 (Yellow). Spider chart by Viziball.

This makes it possible to better reflect his impressive evolution. We can see that he is improving on almost all axes without necessarily extending his go-to guy indicator (which only goes from 68 to 72, i.e., +2.7 minutes per game and + 1.8% usage compared to the previous season).

Diving into a little more detail, we can understand how the Joker manages to increase his impact without playing much longer. First, he improves on the shooting axis, which allows him to directly increase his scoring (almost +5 points per game). He also provokes more fouls, which more regularly brings him to the free-throw line. Finally, he seems to expand his ball control and distribution skills, with a play-making indicator that goes from 75 to 88 (i.e., +2.5 assists per match and -1.1 turnovers).

Finally, we can observe his huge impact in fourth quarters. His _clutch_ness indicator is 100 for the beginning of this season! And it was already very high the previous season. Everyone (starting with the Jazz and the Clippers) remembers last year’s playoffs and Denver’s ability to perform during money-time.

Going further

As you can see, this feature is highly configurable. Obviously, there are things that are not yet possible (like, for example, comparing more than two players on the same spider chart), but the tool is already able to accommodate a large number of combinations, which could bring fans, analysts, gamblers, or even Basketball professionals to optimize their investigation process. You are, therefore, free to express your analyzes through this tool!

I would like to point out that Viziball is a 100% free service that is accessible to everyone without any restrictions. Launched at the end of 2019, this research project led by the French company, Data Nostra, an expert in R&D in the field of data analysis in general (not exclusively in sport), aims to identify innovative methods in the treatment of sports data. The site is under constant development at different levels. We have to deal with the classic issues of a website (UX design, SEO), but also on the basketball analytical aspects. This is the reason why we try to communicate our work as much as possible. To go further, we would be particularly fond of informed feedback from professional sports analysts.

Today, the project runs exclusively on the company’s own funds, which aims to perpetuate the Viziball service. For this, we are open to any partnership proposal, whether with clubs, leagues, or specialized media.

We have just launched a Twitter account, where we regularly share Viziball-based content. This makes it possible to follow the news of basketball with a different statistical axis. I, therefore, invite you to follow us, share our content and experience the tool yourself!


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