Level: Intermediate
Chi-Feng Ho, Student, STEAMs Training center
Mason Chen, Student, Stanford OHS
Age is a big factor for National Basketball Association (NBA) professional players, because an injury could mean a premature end to a player’s career. 36 effective candidates have been selected for study. “Effective” does not mean how strong they are; these players all have had long careers. Our goal is to find methods/information that can help coaches, doctors and even superstars understand the effects of aging. Another goal is to discover why these 36 players can play longer than other players. We use the trends of former players to predict the future performance of active NBA players. We selected three categories on the NBA scoreboard to understand how age affects players’ total game time and total points per season. The total Mins will be used to determine the player's health, because only healthy (no injury) players can have a longer career. The Tpt and PPM show how the player’s performance decreases with age. We then used JMP tools and statistical methods to build up models to derive the correlation between players and their performance related to age. Finally, we predicted the future performance of the current participant and the exact time for the player to retire.
Speaker | Transcript |
| Chi-feng Ho | NBA aging influence and benchmarking analysis. |
| Mason Chen is the co author of the project. The main reason we decided to do this product is that our NBA idol Kobe Bryant was passed away by helicopter accident last year. | |
| Many people start to repay attention to their retirement age, lots of media and fans out Kobe could still play more rather than leave the NBA League. | |
| We want to know if those historical players could keep going about two to three years. | |
| The team might consider a player's maximum contract due to the player's value, current and future performance, given age and previous injury history. | |
| There's no difference between a superstar, or even a bench players. One day they absolutely are going to confront performance decline due to aging factors. | |
| If their team had superstar already faced his data decline should team tickets be still centered on him? | |
| Of course not. The NBA is a business alliance that managers only want to build up a strong team to get an NBA championship instead of losing, won't get any benefits for themselves. | |
| In our project that we want to help coaches, trainers and doctors to find out how important the aging factors is for the NBA players. | |
| From our perspective we will consider is the time good enough for Kobe to leave the leave. Secondly, we consider players who retired too late and some of who retired too early from our data. | |
| Lastly, we worked on how age is important for an NBA basketball players and give some clues. | |
| In our objectives, we want start to build statistical models to show how age is a big factor for NBA players and then we will predict active duty players to performance by using former players' career trajectory. | |
| In the data selection part, we only consider the top players who have complete at least 1,200 games and 15 seasons. And every season should be counted after 1979-1980 seasons. | |
| We exclude the short season records and then we remove any seasons where the players play less than 20 games. | |
| total minutes, the points scored and points per minutes. Three categories. By doing the standardization for each player's year-to-year statistics against the career average. Finally, there are 36 players qualified. | |
| The Career index making and using the standardization logarithm. | |
| Although those players are qualified and counted in our data set, we cannot just use their scores, minutes on stage to to contrast. | |
| After doing the standardization, the group ratio of three categories came out and then we derived the peak career index, | |
| which is equal to Z TM, plus Z PP plus Z PM. The, Z TM is equal to the total minutes of one players, minus the group average. And then we divided by the standard deviation of total minutes. A method for three categories is the same. | |
| You may ask me why we do the Z standardization? Because Z statistics have two benefits. First of all, it will remove any standard deviation bias and it will make sure also to equal weight for players' career average. | |
| This is the pattern for 36 players position distribution by bar graph showing the 36 historical data set of power forward position players are qualified most, which contains amount of 11, and a small forward position players had the least, which only qualified three of them. | |
| On the right, the top, on top graph shows how do we calculate the combo curve and the bottom graph shows the top three players (Kobe Bryant to LeBron James to Michael Jordan) curve versus the combo. | |
| Why do we choose to use three players? Because those players ??? maintain well and they are the best players in history. | |
| By using the average of points per minute, total points to the minutes in each age categories, we combine them | |
| as the combo curve to use to compare to each of the data. It's easy to point out a peak average on the top graph will be 27 years old. And each categories point out the highest value. Kobe Bryant will be a good example to explain that. | |
| On the bottom graph LeBron James shows is an outlier, whose peak age seems to be 21 years old and during the 27 years old, his combo curve dropped to a new low. | |
| The peak season position dependency is to find out the golden age of difference position players. We use ANOVA test to find out the five position dependency was age, same or not. | |
| The dependent variable is to eight factors. We want to know whether there are any difference. Then we do one-way ANOVA. | |
| Is on the figure on the left there is difference between each position of the basketball players; shooting guard and small forward have an early age than other players as well. | |
| The different kind of ultimate strategy most focused on small forward and shooting guard. If the rookie has been chosen to train in those two positions they will have lots of shot and time. | |
| Although the run-and-gun strategy is famous this year, so small forward and shooting guard was still enter their golden age earlier as well. On the bottom | |
| the ANOVA table points out that P value is less than 0.5, which means the probability can reject the null hypothesis to further conclude at least one of the positions has different peak age. | |
| On the right graph, the MVP age is about two years after players peak age. At this time, players experienced their golden age range. We usually use MVP to contrast a player's performance. | |
| As they become older, their body functions will decrease and the injury might influence their mark in MVP selections. | |
| We usee a paired T test along the curve on the age study. We would like to see the connection between players' peak age and their MVP age. | |
| We want to check whether the difference between two measure values of the statistic is not zero. | |
| Our results shows one players who enter his peak age and after two years on average to receive his MVP title. | |
| The peak age and the MVP average age found a significant difference between each of them. A prediction model cannot reject the hypothesis though, suggesting that a prediction is accurate. | |
| Yes, we set the judgments that told us if the player's performance is less than 60 percent of the career average we might think that whether that | |
| person need to decide to retire. If someone in the last year could also have performance scores at 80%, we might think about players could still play two to three years more. | |
| Kobe Bryant left the league was when he was 37. We saw that his performance in last year had maintained about 80% of his ability. | |
| On the right...on the left graph showing a set of players who should retire earlier, but they don't, especially when you go see Juwan Howard's | |
| performance after 34 years old become about 20 to 40% of his career ability. We could point out that Kobe can play more and Juwan should retire earlier. | |
| Our purpose in clustering because we want to use clustering results to help coach and team to see how their players' future performance is. | |
| We used JMP and partitioned 36 players into seven clusters by sixs different categories. The collection of data's object is going to be similar to one another. | |
| Let's look at the graph. This graph compared Gary Payton and Derek Fisher, in some cases. Also R squared value is pretty high to | |
| their own clusters, which means there is a strong correlation between themselves. Based on the R squared value, the graph could clearly show us that they are kind of similar. | |
| Gary Payton retired from league by 2007 and Derek Fisher in 2014. A seven year difference. We might as well use Gary Payton's career trajectory to predict the future tendency for Derek Fisher. | |
| Age is one of the most important factors for NBA players. As players' age increase, they will have a chance to face retirement. | |
| This combo curve model utilize the multivariate statistics clustering and correlation to demonstrate how players' performance changed, based on the difference in age and performance trajectory with age. And the modeling methods could also apply to other professional sports as well. | |
| Thank you. |