Tempo-Free Basketball Statistics

Coach Chambers is trying to change the perception of Penn State basketball, so I'm going to take a stab at increasing the knowledge of Penn State basketball fans. This will be BSD's introduction into tempo-free statistics for the game of basketball. Hopefully some of you will have seen some of this lingo before around the interwebs and already understand it. If you don't, that's the point of this primer. I will be using these kinds of stats all the time during the season (and maybe even later this week), so if they ever confuse you, you can refer to this post. To be honest, this is not going to match the other great TFS intros around the interwebs (like The Only Colors or John Gasaway's). But I'll do my best. 

Who are the masterminds?

The pioneer of modern tempo-free analysis is Dean Oliver. He wrote Basketball On Paper in 2002 and proceeded to become the first person to land a full-time statistical analyst position in the NBA. The leaders of the college basketball stastical revolution are Ken Pomeroy (kenpom.com) and John Gasaway (basketballprospectus.com), among many more bloggers and writers. One of my unanswered questions about Coach Chambers is: does he frequent Pomeroy's site, like other high-profile coaches?

What are Tempo-Free Stats?

To be quite simple, they are what they say they are, free of tempo. The tempo of the game of basketball can obviously influence any of the traditional statistics like points, rebounds, and assists. If there are more possessions in a game, there will be more shots (and misses), leading to more points, rebounds and assists, and so on. Quantifying the pace of a game allows us to proportion traditional statistics, so that they're fairly assessed across the board. 

To do this, these guys have devised formulas to estimate the number of possessions a team has in one game. A possession changes when a team either makes a shot or misses and loses out on the rebound, turns the ball over, or shoots a free throw. The most common formula for possessions is Poss=(FGA-OREB)+TO+(.475*FTA). The .475 weight in the FTA part comes from film analysis from KenPom where he found on average 47.5% of free throws ended in a possession change. This figure varies in many other formulas. To find the estimate number possessions in a game, this calculation is used for both teams' and then averaged. Typically, 60-65 possessions/game is a slow pace (Big Ten), 65-70 is average, and 70-75 is fast. 

Efficiency 

This statistic is a true measure of a team's offensive and defensive ability. It is measured in two ways, although I just primarily use one. The method I like to reference is Points-Per-Possession, which as it says, takes points divided by a game's possessions. A team's offensive/defensive 'rating' or 'efficiency' is the other measure which takes points scored per 100 possessions. Usually breaking even is the average mark for these two statistics. Anything under 1.00 or 100 is good defense/bad offense, and vice-versa. 

Four Factors

A concept developed by Oliver in his book, the four factors analyze what is perceived to be main ingredients to winning a basketball game on both offense and defense: making shots, taking care of the ball, rebounding, and getting to the foul line. 

  • Effective Field Goal Percentage.    eFG%= (FGM + (0.5 * 3PM))/FGA
    This statistic gives an appropriate weight to 3-pointers made, since they are indeed worth more than 2-point shots and should not be counted as the same. The typical average for this stastic is 50%, and ranges from 45%-55%. 
  • Offensive Rebounding Percentage.   OReb% = ORebs/(ORebs + Opp DRebs)
    You know how people still often use rebounding margin as a credible statistic? TFS worshippers typically call it a unicorn stat, because it's completely worthless. A team that shoots 60% is obviously going to have a big advantage over a team that shoots 30% because there are more rebounds available. Rebounding percentage negates that by measuring a team's performance against the available rebounds. The usual average for this stat is around 33% with a common range from 25%-40% . 
  • Turnover Percentage.  TO% = TOs/Possessions
    Obviously this measures the rate at which a team turns the ball over, rather than the sheer volume of turnovers a team has. The more times you have the ball, the more likely you're going to turn it over. The usual spectrum ranges from 15% to 25% for this stat.
  • Free Throw Rate.   FTR=FTA/FGA
    This simple formula displays how often a team creates scoring opportunities from the foul line. I typically just use this raw formula across the board, but it's common for other people to use free throws made for a team's offensive FTR, since it measures the efficiency of a team converting from their chances at the foul line. This statistic varies wildly from game to game, but usually the average range is 25%-50%.

This is a very raw introduction to TFS (also known as APBRmetrics), but these are the most commonly cited statistics, while offering a good primer to grasp the basic concept. You might come across different numbers for these statistics on other websites, but easily the go-to source for TFS is KenPom's site. His numbers for some of these stats are going to be different than if you calculate them yourself with the equations I listed. That is because Pomeroy adjusts his tempo numbers to account for the preferred tempo of a team's opponents. He also adjusts his efficiencies to take into account the quality of opposing teams' offense and defense. 

TFS can also be applied individually to players. That would take another 1,000 words to explain, so you can just refer to KenPom as he explains all the wonderful stats he compiles on each team's page. If you're really interested, just start browsing through his archives to see some of the crazy stuff he comes up with, like an automated scheduler that would raise interest in hoops at the beginning of the year or the minute-by-minute win probability graphs he tracks for every D-1 game. 

I hope this post was informative and opened some of your eyes to the basketball statistics revolution. Now you can all be nerd like me. If only STAT 200 was taught by Ken Pomeroy...

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