Ah, I know what you’re thinking: Ugh, another statistic to digest. Hold up. Now, before I isolate a metric - total points added (TPA) - that will 110% prove what I am writing about *sarcasm noted*, here’s my brief overview on the subject of advanced analytics.
The concept of advanced analytics isn’t fairly new unless you consider the late 90’s the distant past. During that time, analysts such Dean Oliver and John Hollinger sought to combine all of a player’s contributions into one number. Ken Pomeroy’s tempo-free statistics date back to 2002, and his work is often compared to Bill James (baseball).
At the heart of advanced analytics is the notion that by taking into account several measurable (and some assumed) player statistics, their holistic impact can be better understood. In many cases, adjusting data in a tempo-free manner can allow for apples-to-apples comparisons (Pomeroy), while others apply some kind of regression to create an “average player” benchmark (Hollinger’s PER).
My personal opinion on advanced analytics is that they aid in better understanding the game, and are very useful when comparing players from a statistical standpoint. They shouldn’t replace watching games. I once sat on a call with other NBA scouts where one scout spoke about the offensive and defensive efficiency numbers for every player that was discussed but couldn’t add any game context. Advanced analytics should largely support what you’re seeing on the court, expressing it in quantitative form.
With that out of the way, let’s dive into it: Total Points Added (TPA) takes into account offensive (offensive points added, OPA) and defensive (defensive points saved, DPS) effectiveness on a per-possession basis while also incorporating the amount of playing time for the player being measured.
To calculate OPA and DPS, the model takes OBPM and DBPM from basketball-reference.com (estimates the per-100 possessions value of a player) and adjusts for actual possessions a player was on the court for. OPA and DPS are added together to get TPA.
Why not just use offensive and defensive efficiency numbers? Well, here’s an example to help illustrate why TPA is useful:
Player A makes an average team 5 points better per 100 possessions than an average player in his spot, and he plays 500 possessions.
Player B makes an average team 10 points better per 100 possessions than an average player in his spot, and he plays 250 possessions.
From a pure efficiency standpoint, player B is twice as effective. But since player A is on the court twice as much. In theory, they have the same value as they both added 25 points to the average team.
I am not poking a hole in offensive and defensive efficiency metrics, they are very useful. After all, TPA leverages per-possession statistics.
For those of you who have stuck around, what is the TPA for each of the Pittsburgh Panthers (11-5, 1-2)?
Should it surprise anyone that the freshman guard duo of Xavier Johnson and Trey McGowens are leading the team in TPA, no. As the lead guard, Johnson overall TPA is largely driven by his OPA. He generally touches the ball on every possession that isn’t a transition opportunity, and assists on nearly 40% of his teams baskets when he’s on the floor. Playing with the starting unit for a majority of the time also helps his number.
McGowens uses more of the teams possessions in Johnson’s absence (although that’s changing), sometimes due to foul trouble and sometimes to stagger their minutes. While his OPA is still a positive number, one of just three on the team, his overall TPA is largely comprised of his DPS (second best for Pitt). Again, this passes the eye test. He’s the Panthers best on-ball and rotational help defender; he holds the highest steal percentage of any guard at 4.5%, twice that of any of the other three starting guards.
Sophomore forward Terrell Brown may pass junior forward Kene Chukwuka if he continues to be a defensive stalwart for the Panthers. Individually, he benefits from his block percentage of 14.2% and is now logging starters minutes. As Pitt is unlikely to blow out any ACC opponent, it may be hard to move his TPA dramatically as the heart of TPA is possession-adjusted box plus/minus.
Chukwuka’s TPA is higher likely due to logging starter minutes during the non-conference portion of the schedule. OBPM has a subtle way of capturing screen setting, which he’s much better at than Brown. Hence, his OPA isn’t nearly as low as he benefited from setting screens and being on the floor with Johnson and McGowens quite a bit.
The one that may stick out is freshman guard Au’Diese Toney. Most Pitt fans would agree that he’s the third best player on the team, not the eighth. TPA doesn’t take into account the job he didn’t against Louisville’s Jordan Nwora as the Panthers mostly played them to a draw while Toney was on the floor. Offensively, nearly all of Toney’s points come in the flow of the offense, on put backs, or in transition. He’s also a guard having to play a hybrid forward role, defensively.
So is TPA telling us anything about this Pitt team that we didn’t already know? Well, yes and no. Mostly, it’s just quantifying what we’re all seeing in terms of whose been a net positive or negative for the Panthers and in what way.
This has far greater use with more overall minutes played and a larger spread between the players. If Brown had only logged 50 minutes but seemingly far more “effective” when he’s on the floor than Chukwuka at 500 minutes, it might shed light on the fact that they’re adding the same value given the discrepancy in minutes. Visually, plotting this on an X (minutes) and Y (TPA) axis would allow a better visual representation, especially over time.