A Sunday Kind of Piece: Sources of Error in Predicting the Future Wins in the NBA

Posted on 08/08/2010 by

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“I want a Sunday kind of love
A love to last past Saturday night
And I’d like to know it’s more than love at first sight
And I want a Sunday kind of love
Oh yea yea”

One of my favorite things when I was young was the Sunday paper. I loved the size of it. I loved the smell of it. I loved reading it from cover to cover. And most of all, I loved the features. Sunday was the day for in-depth reporting. For magazine style pieces that could be read and reread over a cold glass of lemonade. I also loved the funnies  and the pretty graphs were kind of cool.

To that end I’ve decided that sundays on my blog will be a day for in-depth statistical pieces. I will try to have a thought provoking piece that does something new with the numbers or looks at history in a new light. I’ll also try to get them out early so you can read them with your morning coffee. As always this is 100% guaranteed or your money back 🙂 .

For this post. We’re going to re-visit and expand on some of the issues discussed in my piece on The Talent Pool and Marginal Value in the NBA . Particularly how player performance and talent level can affect future predictions of NBA team performance.

The NBA Market & the Model

As we established in the previous piece, markets in are fickle,variable things. Values and Prices change and move based on an almost limitless and not easily quantifiable set of variables. For  sports, we have markets that are confined to a discrete set of parameters and results. We can measure points, wins, losses . We can make a  reasonably accurate  quantitative assessment of an NBA players’ value to his team and this is what Prof. Berri has done with his Wins Produced model.

It is with this model in hand that we can proceed to make predictions about the future performance of NBA teams. But before we do, I think it’s important that we understand the sources of variation in the model and their possible impact. To do this, let’s take a deeper look at our model.

How the model works is that it focuses on the Marginal Value of an NBA player vs others playing the same position for the particular set. A player is always compared to his peers and his value is measured based on the competitive advantage in wins he provides to his team. A quick way to summarize the model is:

Wins Produced for a Team = Sum for all players on the Team( ADJP48 * Minutes Played/48) – Avg.Team Productivity for the Year + 41

Where ADJP48 is  the player production of wins  per 48 minutes and the average team productivity is the measure of the average team productivity in wins for that particular year. In the Previous post on marginal value we established that:

  • The level of talent at a given position is not fixed over time but rather an ever evolving variable.
  • Average team productivity is a function of that ever evolving talent pool of peers competing against him.

There are four main sources of variation on a year to year basis in the Wins Produced model:

  • Variation in Player Productivity or how well your players play (ADJP48 Variation)
  • Variation in the Talent Pool or the overall quality of talent
  • Minute allocation or how good the coaching and injury luck is .
  • New players or the great unknown.

I’ve talked about the draft at length previously (and I will write more on it in the future) and I will leave minute allocation alone for now . For the rest of this article,  I will focus on overall talent pool. Player performance I’ll save for next Sunday and part 2 (that will give you something to look forward to).

The Talent Pool

In the previous post on the Talent Pool we established that player productivity by position shifts over time. In fact, it looks something like this:

PrettyWhich looks like this in a Chart:

What does this mean for the Wins produced model? Simple, the bar in the NBA is not fixed. Similar levels of gross productivity result in lower/higher net results (wins) based on the level of opposition faced (which is a fairly logical sounding conclusion). It’s harder to win against better opponents. So a 50 win team that stays exactly the same is not guaranteed to win 50 the next year if the league changes around him.

Can we model this variation? If we take the required production by position per year that would result in zero production  and calculate its value in the next season or 82 games at the position we get a general idea of this effect:

You’ll note that the impact by position is generally within +/- 2 wins per team by position (and might be easily discounted as noise when looking at wins produced). However when we look at net impact by team by year:

We see that this becomes a significant factor. A 50 win team in 97 becomes a 61 win in 99 becomes a 50 win team again in 2000. Talent level can cause huge shifts in overall win numbers. So when using wins produced to project future performance for teams it is important to project the league as well or you could wind up missing the target.

See you next sunday for part 2

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