Freelance Friday: Tendencies of Playoff Success and Failure

Jun 15, 2014; San Antonio, TX, USA; San Antonio Spurs forward Tim Duncan (21) celebrates with forward Boris Diaw (33) in the first half against the Miami Heat in game five of the 2014 NBA Finals at AT&T Center. Mandatory Credit: Soobum Im-USA TODAY Sports

Freelance Friday is a project that lets us share our platform with the multitude of talented writers and basketball analysts who aren’t part of our regular staff of contributors. As part of that series we’re proud to present this guest post from Mika Honkasalo, a 21-year old NBA enthusiast from Helsinki, Finland who is studying computer science and mathematics. Mike enjoys big men who can pass and players who can shoot off of screens, find him on Twitter @mhonkasaloNBA.

The playoffs are different from the regular season. Not only are the stakes and level of competition higher but the the game actually changes in some fairly significant ways.

Pace tends to drop around 4 percent, more free throws are taken and teams commit fewer turnovers.

The structure of how you need to win also changes. Unlike in the regular season where the ratio of wins to losses matters, in the playoffs you’re only counting the number of wins you get, since it doesn’t matter how many you lose as long as you get to four in seven before the other team does.

The premise of  this article is to find unique skills, tendencies or traits that playoff teams exhibit that allow them to overachieve their expected win totals in the playoffs, the search for playoff success. The charts below will show different statistical categories relative to league average and plot them against how they fared against estimated playoff wins.

Mika1I built a regression model with weighted values to predict expected playoff wins based on a few different factors including what your conference looks like and how good you actually are to get rid of as much noise in the data as possible.1

The sample size includes the last eight playoffs, which I concluded to be a big enough data set. Mainly because if certain patterns aren’t strongly hinted at in the last eight years of playoff and regular season data, they really aren’t tendencies that provide test cases for modern NBA teams. It’s not a huge sample meaning there’s some noise and findings can’t be taken at 100% certainty, but eight years felt like a good balance between useful test cases and variance. Here’s what I found:

Defense Beats Offense

To start I looked at sort of the low hanging fruit and cliches of playoff basketball, the most common of which is “Defense wins championships”. Surprisingly, not only does defense at the high end of the distribution really help, having a great offensive rating doesn’t help you at all, in fact it is harmful to your expected playoff wins.


Offensive rating actually has a negative correlation with expected wins, even at the high end of the distribution among the very best offensive teams. If you have a top-5 ranked offense, on average you will lose one win from your excepted total.

The difference between being a top-5 offense and having a defense comparable to league average compared to the opposite is a net minus of 3.2 wins. That’s almost one round in the playoffs, which is a massive difference. In fact, in the top quartile and in the bottom quartile of overachievers, there are 75 and 55 percent more top-7 defensive teams than offensive teams.

As a side note, pace doesn’t really impact the equation one way or another except at the extremes of being either really fast or really slow. 60 percent of playoff teams play at a below average pace, and the best place to be is generally in the middle of that area. There’s also some double counting involved here since many of the top offensive teams are fast paced teams. So you can’t play at a snail’s pace or just run up and down (each amount to a loss of -1.2 games) but otherwise you are fine.

The More Superstars the Better


The image above shows the impact of having multiple top 12 players based on Win Shares, which on average amounts to an additional 2.1 wins compared to just having one of those players.

Building a team around one star actually negatively impacts a teams performance vs. expected wins by a win. This doesn’t change even if you have only one of the top-3 players available in that year, bringing statistical validity to the notion of building around multiple stars, though the impact is significantly smaller than the offense vs. defense comparison.

The fact that multiple stars improve your odds of “beating the odds” shows something about teams being able to key in on a single player, which brings flashbacks to the 2011 Eastern Conference Finals where the Miami Heat did anything and everything defensively to get Derrick Rose out of rhythm.

If you don’t have any Top-12 players the result is close to a net plus-minus zero.

Experience Matters

Here’s a really interesting one I was excited to learn about — the average age of the top contributors on playoff teams and how that impacts over or underachieving in the playoffs. This is almost the holy grail of conventional basketball wisdoms and the stats bear out some pretty fascinating stuff.


Of the top 16 of teams that outperformed their expectations, 15 were in the top 10 in age that year. There are mitigating factors that should skew these numbers such as bumps in minutes played in the postseason for older players, but 15 of 16 can’t be an accident.

Funnily enough, before reaching the very top end of the distribution, youth actually helps. I’m not sure if it has to do with fresher legs or if younger teams improve more in-season but the impact is beyond the scope of error enough to be statistically a valid observation that can be said with confidence.

Experience matters if you want to “defy the odds”.

Identity is Everything


The chart above shows the impact of assists on outperforming playoff expectations, but I could have chosen one of nearly every statistic that’s tracked and available on Basketball-Reference.com from defensive rebounding to steal rates and rim protection. What’s notable is that being exceptional in nearly every metric2 helps you outperform expectations.

It doesn’t matter which one, most are a net plus (though there are relative differences between them).

Some notable categories in relative order where team is in Top-5:

1. Assists +1.4 wins

2. Defensive Rating +0.9 wins

2. Effective field goal percentage +0.8 wins

3. Rim Protection (amount of attempts and percentage taken into account) +0.7 wins

3. 3-point percentage +0.5 wins

5. Defensive Rebounding +0.3 wins

6. Lower Turnover rate +0.2 wins

7. Offensive rating -0.9 wins

The most astounding observation is in the case of effective field goal percentage, which creates the inverse effect of offensive rating (a +1.7 wins compared to offense). This points to the fact that execution of offense matters more than how many points you are able to produce per 100 possessions. In a weird sense it’s not how many points you score, but how many buckets you make (adjusted to 3’s of course). Which is an amazing insight that I don’t really know what to make of. My guess it has something to do with the general makeup of teams that are able to execute good offensive sets to generate scores, especially in clutch situations, which helps them excel in a number of other areas. One could point to the Oklahoma City Thunder as a counter example of a team that sometimes struggles to run impressive sets at the end of games which hurts them.

Most of the content of this article has followed conventional basketball wisdom. Defense, experience and superstars are valuable. The biggest deviation from that comes in rebounding, which equals a net zero when you take into account both sides of the ball. Elite rebounding doesn’t seem to matter in the playoffs, or at least in a way which yields high dividends when compared to what is already expected from a team. Even in the extreme where take the top half of rebounders and compare it to the bottom half, there’s literally no difference.

Assists are actually the most important simple stat (Go Spurs!) when looking for teams that have a chance to outperform in the playoffs. The takeaway here is that whatever you do well, you should strive to do even better when the playoffs start. Identity is everything.

Though there are some things that are relative valued higher than others, which can be taken with a grain of salt due to sample size, being exceptional at one or two things and then building focusing on and getting them to translate in the playoffs is more important than anything.

In the top quartile of teams in expected wins, the ones that didn’t have any stat in the top 3 are expected to win 2.4 games fewer than teams with at least one– the value of over half a series.

*Link to SQL Schema in HTML here. All Stats per Basketball-Reference.com. Charts Created on Zizhujy.com


  1.  Regression Model Standard Deviation 2.74 in Expected Playoff Wins. For reference being Top 5 in Simple Rating System amounts to +0.2 wins, meaning the advantage of just being very good is small and gives me confidence in the model.
  2. Exceptions being offensive rating, offensive rebounding, shooting 3’s (attempts, not percentage) and pace

Guest Post

  • gustavo iglesias

    The ORTG stuff is surprising, to the point I can’t really believe it.
    Maybe there is some team which has big ORTG but often underachieve (for whatever reason) and skew the data?

    Because it is really against common sense “hey, let’s trade away our two best offensive players, so we can go further in Playoff”

    • Looked at it and it doesn’t seem like it. ORTG just doesn’t look like it translates into the playoffs.
      I mean I guess just in theory if you traded your ORTG guys “into” equivalent DRTG guys mathematically you’d come out on top. But only equivalent because of course if you trade good players you lose “expected wins” also.

      • supertriqui

        I can get that ORTG doesn’t translate. It’s counterintuitive but can get it: bulls, indiana, memphis are good examples. What makes me hard to swallow is that it is a *negative* effect.

        I mean: trading a guy with 105 ORTg and 105 DRTG for some other with 100 and 100 is better because defense matter more. Ok. I get that. But this seems to say that trading the guy with 105 ORTG and 105 DRTG for the guy with 100 ORTG and 105 DRTG is a good option too. It’s not that ORTG doesn’t translate well: it is harmful

        • I’d have to go through the logic a bit more but just quickly what came to mind was that no it doesn’t say that because a 100ORTG 105DRTG team would have a lower expected win total from which to start the comparison.

          There’s some selection bias stuff and other things roaming around in my head also that I’m not sure about.

          I’m not 100% sure but I think that sort of thing isn’t a problem in this analysis.

          EDIT: Yeah so it’s not that offense has a negative correlation with expected wins but in beating those expected wins. Key difference.

          I think…

          • gustavo iglesias

            Now THAT makes sense in my head. 🙂
            Something like “having a good ORTG will place you in RS above your “real” place”. So you begin the PO with expectatives which arent easy to acomplish, because your record is inflated by the balloon effect that good ORTG have in RS. That makes sense.

            Good communication, btw. Keep up the good work!

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  • bballsmarts

    The whole efg% thing was baffling. Doesn’t efg% and ORTG pull each other in one direction? one should help the other

    • Dean Oliver’s Four Factors has the correlation at around 40% I think, I’ve personally done the analysis/exercise and get to somewhere around and above 50%, meaning they do have a high correlation with each other. So the effect is actually watered down.

      Don’t know if I sold it well enough but that fact is probably the single most important finding in the thing.

      • nick restifo

        wouldnt you say that the biggest difference between ortg and efg% is points scored at the line? wouldnt the simultaneous benefit of efg and the detriment of ortg in the playoffs point to the teams that depend on getting to the line more than usual for a good ortg?

        • In a technical sense yes. And that’s the conclusion to arrive at when you think about it. However the free throw numbers only explain some of it, about 35% (depending a bit on interpretation), but the rest is left out. So the remaining two-thirds looks more like teams that have high efg% exhibit other tendencies that aren’t measured (at least in common metrics) that help.

          It’s a bit weird which is why I said I don’t know what to make of it.

          A part of it might be crunch time “swallow the whistle” type of scenarios, but honestly I’m not sure.

          Sorry I couldn’t give a better answer. A full explanation could be a piece by itself.

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