Notice how much better this group does handling new player combinations: Another major takeaway here is how relatively close all these stats are, especially compared to some of the Old Guard metrics from the previous group. Career Opportunities. There’s nothing inherently good or bad about more stability. There’s actually a larger issue with those prior tests that I wanted to suss out.
It uses 2015 to predict 2016, 2016 to predict 2017, and so on. So unlike prior versions of this test, I focused on new teammate combinations, such as James to the Lakers.
In other words, if LeBron James averaged 27 points per game last year, should we expect him to average 27 per game this year? This includes ESPN’s RPM, which I’ve omitted due to sample size (it’s only five years old in its current version). (If you’re wondering about sample size, there were 225 teams in the under-72 percent continuity group.).
Now look at our dummy metric, iSRS, which uses only minutes and the team’s result to evaluate players. Your privacy is safe with us.
We present them here for purely educational purposes. Among those teams, APM is well ahead using this testing method, with an average error of only 2.4 points per squad.
What we have today is still incredibly helpful — we can see this in how much smarter forecasting systems are. It’s helpful to understand how team circumstances can influence a statistic, so I compared each metric’s variability when a player changes teams to when he remains on the same team with similar teammates — in this case, defined as having 85 percent of the same minutes return from the previous season, per Basketball-Reference’s roster continuity calculator.
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Our reasoning for presenting offensive logos. iSRS arguably outperforms points per game here, so it might be wiser to cite minutes-on-a-good-team instead of raw points as an indicator of player quality. We’ll never be able to nail a retrodiction test like the ones above with physics-like precision, because players are injured, grow older and susceptible to variability in physiology. It’s implausible that these stats are correctly pegging a player’s circumstantial value — that Kevin Love really was that indispensable among his specific teammates in Cleveland and then his value shrunk in 2019 without them — because this would have to happen while the inverse occurred for LeBron in LA, and then that phenomenon would need to repeat itself among hundreds of critical player-seasons. Like PER, Win Shares and Wins Produced are far less predictive with lower continuity lineups. Wins Produced (WP) and Win Shares (WS) have a nearly 2:1 ratio, whereas stats like PIPM and APM are fairly similar in variability, regardless of new teammate circumstance. It’s only in 2019, when the rosters were shuffled significantly, that its claim about Love and James could be exposed (e.g. We can see this clearly when looking at the results since 1978, long before the plus-minus era (the previous graphs spanned 1998-2018): These are all upgrades over the best of the Old Guard metrics, Win Shares. But “goodness” is about a player’s average impact from all relevant team scenarios. Since 1997, there have been 41 teams with an SRS of at least 4 and a lineup continuity of at least 88 percent. Data Provided By RPM looks about as good as these other top metrics in these tests, but certainly not better based on a few seasons of information. The Bulls defeated the Lakers 104-83.
In other words, if a metric cannot reshuffle its players and accurately predict new player combinations, then it’s likely misallocating credit in the first place. Your favorite teams, topics, and players all on your favorite mobile devices. As expected, iSRS also falls apart when the core changes more, largely because high-minute role players riding the coattails of stars can’t replicate the same iSRS value on other teams.
Or write about sports? PPG: Points per game, the original player ranker, iSRS: individual SRS, a dummy metric that multiplies a player’s minutes by his team’s SRS (adjusted point differential). On the opposite end of the spectrum is the dummy statistic, iSRS, which is incredibly sensitive to new team changes.
Imagine a metric that thinks Kevin Love was much better than LeBron James while they were in Cleveland together. Question, Comment, Feedback, or Correction? Are you a Stathead, too?
This is not because it’s a superior stat, per se, but because plus-minus data is noisy and lacks the box score information used to stabilize AuPM.
These are strong indicators that the plus-minus family is measuring something quite important, even if it can be difficult to tease out the noise at times.
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PER, which is heavily influenced by volume scoring, clocks in as the second-most change-immune metric.
Add that number to his new Laker teammates’ projected wins (based on each of their 2018 Win Shares) and we can predict the 2019 team performance.
Besides feeling like alphabet soup, what do these all-in-one stats really measure, and how valuable are they for player analysis? But look at what happens as the core is changed more and more — PER struggles to predict the new combination of players. DraftKings daily picks: Fantasy basketball lineup – January 8, 2019, The Whiteboard: Derrick Jones Jr. has potential as big as his wingspan, Basketball-Reference’s roster continuity calculator, Nylon Calculus: James Harden and True Shooting Charts, The Balance of the Spurs' Season Shifts with Its Defense, Nylon Calculus: The NBA players with the weakest supporting casts, Nylon Calculus: The shooting efficiency of the San Antonio Spurs. Watch our How-To Videos to Become a Stathead, Subscribe to Stathead and get access to more data than you can imagine. “Value” describes the impact a player has in a given circumstance (e.g. The Bulls defeated the Lakers 104-83.
Bryant, who is 19 and bypassed college basketball to play in the NBA, scored a team-high 33 points off the bench, and Jordan scored a team-high 36 points. Let’s start with the right side of the chart, when teams essentially return the same core. I think there’s something larger going on here, and it speaks to the future of all-in-one stats in general.
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The idea is to take each player’s rating in a stat and predict his contribution in the next year based on the number of minutes he ends up playing.
If we do this for every team, we can calculate an overall average error for each metric. To investigate this, I grabbed the following metrics for every player-season since 1997 (unless otherwise noted) in attempt to glean value and a deeper understanding of these numbers: I started by looking at a metric’s stability from year-to-year. On the other hand, a metric that is insensitive to team change might be accurately measuring a player’s overall “goodness” (irrespective of circumstantial value), or it might be rigidly measuring something else that is consistent, but inaccurate. Unlike the previous metrics, APM doesn’t completely fall apart when lineups are shuffled, exhibiting a much softer decline in prediction error as lineup continuity is lowered. Subscribe to our Free Newsletter, This Month in Sports ReferenceFind out when we add a feature or make a change.
(PIPM and AuPM are tied for next best at 2.8.) Advertise on NBA.com | Yet it still outperforms PER here!
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