The Winning Formula: Age and Experience

Though the analytics first broke into the sports world in baseball thanks to Bill James, sabremetrics, and Brad Pitt (Billy Beane), basketball is now riding the wave as well, and arguably even higher. Like the MLB (and most other major sports), NBA front offices now have a statistical and analytical focus. Teams and coaches are viewing players through different, more diagnostic, lenses. Writers such as Zach Loweand Kevin Peltonare doing what Jonah Hill (Peter Brand) did for sabremetrics, translating the complexities front office and coaches are analyzing on a daily basis into layman’s terms. Being an avid NBA fan and typical nerd, I have grown to admire the analytic work done by the basketball community, so much so I decided to give it a try myself…

As a Statistical Science major here at Cornell, I am conducting an independent study, with ILR Organizational Behavior Professor Emily Zitek, looking at the effects of NBA team composition and performance on its overall success. Essentially, what is “The Winning Formula”? In a series of blog posts within the next few weeks, I will discuss many factors that contribute to a team’s success, such as experience, age, Pythagorean Wins, Win Shares, standard deviation of performance statistics and Dean Oliver’s Four Factors. Prerequisite knowledge is not needed, nor is a degree in statistics, only a curiosity as to why your favorite NBA teams perform the way they do. Though I will not make any groundbreaking conclusions, I hope to paint clearer picture as to why certain teams are successful and others are not.

Two of the most frequently cited factors that determine success are age and experience. That team is too old (Lakers). That team is too young (Bobcats). That team is too inexperienced (Rockets). That team is so veteran-savvy (Spurs). Does all of this theoretical hypothesizing done by the media have factual merit? I tried to take a statistical approach to answer that question by analyzing team data from the past 11 seasons, from the 2002-2003 season until the just-finished 2012-2013 season.

First, it is important to understand that experience and age, though correlated, are two very different things. Chris Copeland was a 29-year-old rookie this year, with 0 years of NBA experience. John Wall and Rookie Damian Lillard are the same age, though John Wall was drafted 3 years ago.

Most citations of “youngest” and “oldest” team have to do with team averages. However, when looking at the age and experience of a team it doesn’t make sense to calculate the average age or experience amongst its players. Why should Grant Hill (Age 40, 17 years of NBA experience) and his 437 total minutes played this season have the same impact on Clippers’ age and experience as Eric Bledsoe (Age 23, 2 years of NBA experience), who played more than three times as many minutes (1553) as Hill? Therefore, by weighting a player’s age and experience by the percentage of his team’s minutes he played during the season, I created two new variables, Weighted Age and Weighted Experience. In this example, Eric Bledsoe’s spry 23 years get three times as much weight as Grant Hill’s ancient 40 years. Essentially, Weighted Age represents the average age of all 5 players on the floor at any given time throughout the season. The results of the Weighted Age and Weighted Experience calculations for Playoff and Non-Playoff teams in the last 11 seasons can be seen below.

As you can see, playoff teams, on average, give minutes to players 1.42 years older and with 1.47 more years of experience
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Weighted Age also allows us to look at another interesting team characteristic, the Age Premium. This is the difference the between the Weighted Age and Average Age of a team, or essentially the emphasis they put on age. If a team has a positive Age Premium, they are playing their older players more minutes. The same reasoning obviously applies for the Experience Premium. Playoff teams have higher Premiums that Non-Playoff Teams, illustrating that not only do Playoff Teams play older and more experienced players, they give their elderly players a greater share of the minutes than Non-Playoff Teams.

For the entire NBA, the negative Age Premium(-0.05) and positive Experience Premium(0.378) illustrates an interesting trend. Over the past 11 seasons, teams, on average, are giving playing time to players with more experience, yet slightly less age. This tells us front offices and coaches value greater NBA experience, but not greater player age. Why? As I will demonstrate, experience has been proven to be better indicator or success.

I ran simple and multiple regressions on the above variables to try to predict team winning percentages. I used a lagged dependent variable of the previous season’s winning percentage to take into consideration how good the team was last year and included a random intercept to model the non-independence amongst teams. Average Age and Average Experience were not used because, as previously stated, how is a 40-year old Grant Hill sitting on the bench for most of the season going to help you win? The below table gives the regression coefficients (standard errors in parentheses) with regular season winning percentage as the dependent variable.

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It can be seen that both Weighted Age and Weighted Experience are significant indicators of success considering last year’s performance. Due to the standard deviation of Weighted Experience being 1.6 years, regression 1 predicts that a team one standard deviation above the mean of Weighted Experience (6.698) can expect to win about 10.17% more of its games (8.34 games in an 82-game season) than a team one standard deviation below the mean (3.498). Age has a similar, but not as significant impact. Given the 1.68 year standard deviation of Weighted Age, regression 2 suggests that a team one standard deviation above the mean of Weighted Age (28.41) can expect to win about 9% more of its games (7.38 games in an 82-game regular season) than a team one standard deviation below the mean (25.05).

The strength of experience over age is stressed again when both are included in multiple regression model 3. Due to the correlated nature of Weighted Age and Weighted Experience, I can’t make a claim about an increase in wins predicted by a given increase in Weighted Experience, like in the previous paragraph. The coefficients can no longer be interpreted literally, yet they still show important concepts. After taking into account Weighted Age, Weighted Experience is still significant, however, after taking into account Weighted Experience, Weighted Age is insiginificant. Essentially, once you account for a team’s Weighted Experience, Weighted Age doesn’t explain any more of the variability remaining in team winning percentages. However, even if given Weighted Age and the previous year’s winning percentage, Weighted Experience can still add a lot to the picture.

Regression 4 mathematically proves an observable concept. Given how good a team was last year, their Experience Premium (the emphasis they put on age) is a significant predictor of success. Essentially, if a team is playing its players with more experience, it is more likely to be a better team. If a team is playing its younger players the majority of its minutes, it is more likely to be a bad team. This makes intuitive sense; teams with negative experience premiums are probably in their development stage, hence giving playing time to younger players, and teams with positive experience premiums are most likely built to win now. However, this is not true for all teams (this year’s Thunder have a negative Experience Premium for example), and as with most regressions, this is meant to show a trend, not the rule.

We’ve figured out that both having older, more experienced players on the floor tends to improve your chances of winning in the regular season, but for most NBA franchises (spare the Bobcats), the real goal isn’t to succeed in the regular season and make the playoffs, it's to win a championship. I ran the same four regressions as earlier, but used Playoff Wins, a perfect barometer of playoff success, as the dependent variable. This time, none of the independent variables besides previous winning percentage were found to be statistically significant in predicting playoff success. What does this tell us? Having an older, more experienced team is beneficial in the regular season, but doesn’t seem to have much of an effect in the playoffs. In fact, I even ran the regressions trying to predict regular season wins while restricting my sample to only teams who made the playoffs, and neither Weighted Age, Weighted Experience, nor the Experience Premium has a significant impact. Age and Experience are what separates the good teams from the bad teams, but doesn’t pull a leader out of the front of the pack.

These findings coincide with the conclusions made by James Tarlow in a paper titled "Experience and Winning in the National Basketball Association" presented at the 2012 MIT Sloan Sports Analytics Conference . However, Tarlow went even further, looking at team chemistry and coaching regular season and postseason experience. He found, unlike player experience, those variables are significant predictors of playoff success. I suggest giving the article a read.

It shouldn’t be shocking that experience matters more than age in the NBA, at least with regards to regular season performance. In fact, I would hypothesize that it matters in every sport and professional endeavor. NBA GM’s know this and build their rosters accordingly. A player’s old age is a detriment when entering the NBA Draft. Age for a player like 23 year-old Gorgui Dieng entering the draft this year is a clear disadvantage. A player like Dieng needs to develop, so by the time he has adequate experience he may only have a few years left in his physical prime.  On the other hand, Nerlens Noel is 19 years-old. NBA teams would love to get their hands on him and have his peak physical performance coincide with a sufficient level of NBA experience in his mid-20’s.

The takeaway here though, is that this is a trend, not the rule. You can be a very young and inexperienced team and make the playoffs, look at the Rockets this year. However, as James Tarlow argued, lack of coaching playoff experience and team chemistry will be a detriment. But, just for fun, here are the least experienced and youngest teams to get in the playoffs in the past 11 seasons

 

As you can see, the Thunder tend buck the trend. In fact, the 2011 Thunder won the most (9) postseason games out of all the above teams.  So what’s the real solution? Get Kevin Durant on your team. In all seriousness, this points to a significant point. Though you may have just wasted ten minutes of your life reading this post, there are many other factors besides age and experience influence winning and losing in the NBA. In fact, the R² for best model (Regression 3) is .393, meaning only 39.3% of the variability in regular season winning percentages can be explained by a team’s Weighted Age, Weighted Experience and their previous winning percentage. So even though a team with “savvy veterans” may do better over the course of the regular season due to their experience, please realize this is not the only reason why. They may have Lebron James.

Contact Joey Shampain at joseph.shampain@gmail.com with any questions