I was curious who played for a single team over their entire career, who skipped around, and how the patterns changed over the decades. Read More
Category Archives: basketball
This map blends two of my passions: cartography and hoops. The elevation surface on the map is derived from the most common scoring areas in the NBA during the 2019-20 season. Higher places indicate the areas where NBA scorers scored the most from. Naturally this includes the areas near the rim and the areas just outside the 3-point line.
The original plan was to make a fun map poster emphasizing the best scorers from the 2019-20 season, but the project quickly spiraled out of control as I started to label more and more historic places.
It seems like there’s been more player movement than usual over the years. Didn’t players used to play on a single team for the entirety of their careers? Read More
FiveThirtyEight publishes win probabilities for NBA games throughout the season. During the playoffs, they show chances of winning each round, and with each game, the probabilities shift. Adam Pearce animated these shifts, from the start of the playoffs up to now.
Nice. The visualization. Not so much the Lakers.
Inpredictable, a sports analytics site by Michael Beuoy, tracks win probabilities of NBA games going back to the 1996-97 season. When a team is up by a lot, their probability of winning is high, and then flip that for the losing team. So for each game, you have a minute-by-minute time series of win probability.
Beuoy added a new feature that looks for games with similar patterns a.k.a. “Dopplegamers”.
FiveThirtyEight has been predicting NBA games for a few years now, based on a variant of Elo ratings, which in turn have roots in ranking chess players. But for this season, they have a new metric to predict with called RAPTOR, or Robust Algorithm (using) Player Tracking (and) On/Off Ratings:
NBA teams highly value floor spacing, defense and shot creation, and they place relatively little value on traditional big-man skills. RAPTOR likewise values these things — not because we made any deliberate attempt to design the system that way but because the importance of those skills emerges naturally from the data. RAPTOR thinks ball-dominant players such as James Harden and Steph Curry are phenomenally good. It highly values two-way wings such as Kawhi Leonard and Paul George. It can have a love-hate relationship with centers, who are sometimes overvalued in other statistical systems. But it appreciates modern centers such as Nikola Jokić and Joel Embiid, as well as defensive stalwarts like Rudy Gobert.
I’ve mostly ignored sports-related predictions ever since the Golden State Warriors lost in the 2016 finals. There was a high probability that they would win it all, but they did not. That’s when I realized the predictions would only lead to a neutral confirmation or severe disappointment, but never happiness.
I’m sure this new metric will be different.
Wondering whether if a player’s shot improves over the course of his career, Peter Beshai shows shot performance for all players from the 2018-19 season:
To understand whether or not a player actually gets better over time, we need some kind of baseline to compare their current performance against. On Shotline, the baseline is set after a player completes their first season in the NBA and has shot at least 200 times. This may sometimes feel a bit arbitrary, and I guess it is, but it feels reasonable to compare a player’s first season’s performance to their current to understand whether they have improved or not. The graphs are set up to allow you to compare their current performance against any other point in time too if the baseline is not sufficiently interesting to you.
During a game, the range of emotions can vary widely across a crowd. Will Hipson, making use of some emotion dynamics, simulated how that range can change through a game:
What I’m striving to simulate are the laws of emotion dynamics (Kuppens & Verduyn, 2017). Emotions change from moment to moment, but there’s also some stability from one moment to the next. Apart from when a basket is scored, most fans cluster around a particular state (this is called an attractor state). Any change is attributable to random fluctuations (e.g., one fan spills some of their beer, maybe another fan sees an amusing picture of a cat on their phone). When a basket is scored, this causes a temporary fluctuation away from the attractor state, after which people resort back to their attractor.
I want to simulate emotion dynamics for all the things now.
Ivana Seric is a data scientist for the Philadelphia 76ers who tries to improve player effectiveness by analyzing tracking data. Aki Ito for Bloomberg:
I really want to see the relationship of winning and teams who more deeply follow statistics. Is it at a place yet where this actually helps or is still more about gut and heart?