A Quick Look at the Last Two Minutes Reports in the NBA
Who is screwed? Who is favored? And a new record is reached.
January 22nd, 2024 marked the date when the Hornets and the Timberwolves played a ridiculous game in many shapes and forms. Karl-Anthony Towns scored 62 points, and yet the Wolves blew a lead of 18 points that they’d accumulated by the end of 3rd quarter. A day later we were blessed with an L2M report which consisted of 10 total errors, 6 of which harmed the Wolves.
The Record Holders
That game was chaos, during one play multiple missed calls seemingly harmed both teams, however, the whistle was silent and the play went on with its way. On Towns’ final drive to the hoop, the L2M report states that he was fouled two times, but yet again, the whistle was silent. That would’ve been a huge shift to the game flow as well, as Kat was “blocked” on that drive and the Hornets went on to secure the win with the free throws.
This chaotic game is 4th all-time when it comes to total errors that happened during the last 2 minutes.
The record game happened on 25th March 2016 with 14 errors, 9 of which went against the Wizards. However, the first three matchups all went to 2 or more overtime periods, and thus automatically offered more room for error - since reports go the last 2 minutes of 4th quarter and each subsequent overtime.
With all that in mind, we can say that the game between the Hornets and Timberwolves is the game with the most errors since L2M reports started to surface - if we normalize the errors per period. If someone is counting all the records in the season, you can add this one to the list.
The Ongoing Season
But that game was a big outlier regarding regular L2M refereeing and correctness. When we take a peek at the entire season (up to and including games on 2024-01-27) the Hornets are mid-pack, while the Wolves generally get a bit more lenient whistle despite “cheating ass refs” as Anthony Edwards stated after their win vs the Thunder even though there were no errors in the L2M.
The biggest winners when it comes to the errors in the L2M are the reigning champions Denver Nuggets. Vox populi on this topic (as observed in some of my recent tweets on this topic) is that the Nuggets are getting ripped throughout the game so that refs make up for it towards the end of the game. That isn’t that far-fetched idea as the Nuggets are only 24th in Free Throw Rate despite being among the top of the league at shots near the rim (4th as a team in shots with distance <=5 ft.). Also, the Nuggets paid their share in the recent seasons during the last two minutes, so we’ll say that nature is coming to balance with this outcome.
The New Orleans Pelicans seem like the biggest losers of the current season, with 13 calls going against their favor in comparison to 3 going for them. Interestingly enough, the Philadelphia 76ers are right behind them, and after that, it kind of normalizes slowly.
The Individuals
A logical thought that follows after the previous chapter is which individuals are being favored/hindered. To analyze this, we’ll go a bit deeper and observe data from the last 4 seasons.
There aren’t many players who have more calls that hinder them, and interestingly enough, two of them are the current top MVP contenders - Nikola Jokić and Joel Embiid. Also, notice how there is a large number of (first option) centers on the top of this list - that makes sense because L2M reports go both ways - offense and defense, and centers are still the focal points of defenses and they can often be called for fouls, defensive 3 seconds, etc…
The general trend is that these players get more calls that go in their favor. However, that isn’t the case only among the high-volume players. Among 129 observed players that played at least 20 minutes per game in each of the past 4 seasons, 89 of them had more errors that went in their favor, and only 31 had more errors that went against their favor. The remaining 8 players had an equal number of calls that favored/hindered them.
When we look back at these last two charts, it’s far less likely to assume that some team is being targeted/favored intentionally than it is to assume that refs can make an honest humane error.
The Refs
Everyone makes mistakes, NBA players are constantly late on rotations and closeouts, they misunderstand each other and they break the rules. They don’t do it on purpose. The same goes with referees.
Among the top 20 referees in total games officiated (in the regular season) since March of 2015 (when the L2M reports came out), no one stands out, both positively and negatively. Which is good.
The difference between Matt Boland and Mark Lindsay is 0.34 errors per game, which would equate to an extra error every third game more or less. But that margin of error could be even smaller since we don’t know if Boland or some other accompanying referee made the call.
Through play-by-play data, we might be able to find out who made the Incorrect Call, but the bigger issue is in Inaccurate Non-Calls, they make up most of the wrong calls anyway, and if you think about it, the entire trio is guilty of that, as everyone had the opportunity to make the call.
The Future and the Past Work
There are a bunch of ideas and approaches worth discovering and revisiting when talking about L2M reports, here are a few of them:
Go deeper regarding the refereeing decision-making and find out who makes the most incorrect calls (possibly by scraping play-by-play data and connecting it to L2M reports)
Check what team a ref possibly “targets”
Breakdown by each player - both for offensive and defensive inaccurate calls
Some of these ideas have already been covered to some (or full) extent in these past readings:
Do NBA Superstars Really Get All the Calls? - Peter Li
You’d Complain Too - Owen Phillips
NBA Last Two Minute Report - Russell Samora
>The difference between Matt Boland and Mark Lindsay is 0.34 errors per game, which would equate to an extra error every third game more or less.
I feel like when people disparage a referee, it's usually not because of their perceived average error rate, but rather their *inconsistency* e.g. "If so-and-so called that on one end, they should call it the same way on the other". In statistical terms, wouldn't that make the variance of a referee's errors in a L2M report more enlightening than their average errors per game?
Do you have access to the raw data so we could see the average standard deviation of errors per game and how each referee's individual variance compares?