Mystery in Milwaukee

The Bucks need to be more patient if they hope to become a contender

When Brandon Jennings picked the Bucks to beat the Heat in 6 games in the first round of the 2013 NBA playoffs, everyone thought he was out of his mind. Jennings' delusion is representative of the franchise's overconfidence in its ability to compete with top level teams. That mindset was on display when the Bucks traded for J.J. Redick at the trade deadline.

Prior to the trade, a number of contenders were rumored to have interest in acquiring Redick. It made sense for teams like the Spurs, Clippers and Pacers to trade a draft pick or a prospect for a dynamite 3PT threat and a solid perimeter defender to improve their chances at making a deep run in the playoffs. The Bucks had an extremely small chance at making it to the finals at the time of the trade deadline, however, they were the team that ultimately traded young prospects (Tobias Harris and Doron Lamb) to acquire Redick.


Some teams were wary of sacrificing too much for Redick because he is in the last year of his contract and he will become an unrestricted free agent in July. That meant that he might only play for whichever team traded for him for a few months. Perhaps the Bucks thought that having Redick for a few months would help them to keep him in Milwaukee. Teams have a financial advantage over the rest of the league when it comes to re-signing their own free agents because teams are able to offer more money to their own free agents than any other team. However, Redick is not a max-level player where this would be much of a factor. The offer that Reddick receives from the Bucks will likely be similar to the offers that he receives from other teams.

Therefore, the Bucks could have simply waited until free agency to offer Redick a contract without having to party ways with any of their assets. The Bucks elected to get rid of Tobias Harris, who has two years left on his contract and who will then become a restricted free agent, which means that the Bucks can match any offer that Harris receives when his deal expires. It is much easier for a team to retain a restricted free agent like Harris will be than it is to retain an unrestricted free agent like Redick. Basically, the Bucks gave up a player with two more years on his deal who will be much easier to re-sign for a player who had a few more months on his deal who will be much more difficult to re-sign.

The Bucks must have thought that Redick would dramatically improve their team because there would be no other reason for them to trade a solid young player like Harris to get a few months of playing time from Redick. That is why it is inexplicable that Redick only played an average of 17 minutes per game in the Bucks' 4 game playoff series. Did the Bucks really think that 17 minutes per game from J.J. Redick would help them in the playoffs?

This is not a new phenomenon in Milwaukee either. At last year's trade deadline, the Bucks traded traded oft-injured center Andrew Bogut for Monta Ellis (in a deal that involved several other minor parts). Rather than trading Bogut for young players and pick, the Bucks opted for Ellis because they must have thought that Ellis would help them become a contender in the East. The Bucks would have been better off trading for players who would help them in the future rather than Ellis, who has been good enough to help Milwaukee finish 9th and 8th in the East in the last two years, respectively.

The acquisitions of Ellis and Redick helped Milwaukee finish in the "dreaded middle" (8th to 10th best team in the conference), where they've been stuck pretty much every year of the Brandon Jennings era (2009-present). This summer they must decide if they want to cough up big money to re-sign Jennings (who is a restricted free agent). Jennings has pretty much established who he is as a player at this point in his career; a speedy shoot-first point guard who can distribute the ball but who has always shot at a low percentage. He is good enough to get Milwaukee into the playoffs but not good enough to get them much further than that.

 If that's their goal then they should go ahead and pay Jennings and Redick and let history repeat itself. If they want to go further than that, they need to get bad before getting good. That means letting Jennings go, trying to sign Redick to a reasonable deal and using next season to develop some of their young players such as Ersan Ilyasova, Larry Sanders, and John Henson. Without Jennings, the Bucks should be a lottery team and the 2014 draft class is shaping up to be a good one. Becoming a high caliber team takes years, and if the Bucks want to become anything resembling a contender, they need to take a step backward before taking two steps forward.

Brandon Jennings' overconfidence is symbolic of the team's misconception of its own ability. Re-signing Jennings to a large contract will only confirm that they still think they are better than they actually are. Either that or the Bucks front office simply does not have the patience to build a great team.

Jockeying For A Better Future

Dangerous conditions and scant pay continue to plague jockeys, the workers of the horse racing industry.



It is a profession in seemingly unending decline.  Its union has been ineffective, to the point of corruption.  No, this piece is not discussing longshoreman or locomotive workers, but America's jockeys.  The frontline of the nation's horse racing industry faces problems not seen in any other major sport in the United States.  Jockeys struggle to deal with low pay, a myriad of injury and health concerns, and a decreased demand for their services, even in an industry that remains unabashedly extravagant.

Jockeys understand that their jobs entail significant risk.  Their line of work involves racing half-ton animals at a speed of 40mph.  They choose jockeying for the excitement, the glamour, and their love of horses.  Like in other sports, the competition is fierce, and only the best make it to top.  Careers are necessarily short as injuries mount and performance suffers for aging jockeys. 

So how much do these brave athletes make for their dangerous work?


Not much. The median yearly salary has been reported as low as $30,000. Most jockeys ride for less than $50 per mount.  After fees for equipment managers and agents, many take home less than $20 for lesser races.  Riders do keep a percentage of race winnings, however, these are of course normally won by only the best jockeys who get to ride the best horses. The top 100 jockeys in the country make 57% of the profession's revenues, for instance. While this year's Kentucky Derby featured a prize of over $2MM, many riders have a better chance of becoming injured (20-1), than of winning the race.

In fact, a recent report from the National Institute for Occupational Safety and Health examined specific concerns related to jockeys' health.  Among health issues, trauma from falls and other racetrack accidents were the greatest reason for missed time.  The study also found that the repetitive and strenuous motions of horse riding could have severe effects on jockey's joints and bones.  Finally, the prevalence of eating disorders was singled out as an area of particular vulnerability.  Minimum riding weights were felt to encourage unhealthy habits, like vomiting and substance abuse. And all of this for a $30,000 a year job that is highly competitive and specialized in an industry with high revenues and rampant financial frivolity. 


The parent company of the Kentucky Derby announced revenue of over $270MM in the quarter following last May's Derby weekend. It seems odd that the Jockeys' Guild, a jockey specific union, would not be able to get a larger chunk of that cash for the workers who actually make the race happen.  But the guild has struggled due to chronic mismanagement, and recently went through bankruptcy proceedings.  Needless to say, it has not posed an economic threat to the industry. 

Politics, another possible avenue for change, are particularly important in horse racing, with many states completely controlling facilities or regulating them.  But since the Jockeys' Guild is so small (1200 members), and has such limited resources, it's difficult to see inroads being made through lobbying.  In fact, only four states include jockeys as part of their workers' compensation policies, a necessity for a profession so dangerous. Eating disorders and abuse of substances like laxatives and diet pills are also a major concern, although most states still have relatively low minimum weights that exert a downward pressure on jockey's health.  Requirements for protective equipment are still relatively new and not particularly convincing. 

Overall, few jockeys leave the sport in good physical and financial condition. While there are certainly health risks inherent to the industry, riders are not compensated in a way that recognizes the bravery and talent they possess.  A stronger union and more sympathetic government are needed if the workers of the horse racing industry are to be treated equitably.

The Winning Formula: Balance

It may be the trendiest topic in the NBA: balance. Do you need it? Do you want it? What really is it? Every NBA GM, analyst or fan may have a different opinion. Should a team be built around one (or more) superstar(s) or should it be evenly assembled with complimentary parts? Not only has it become popular for superstar players to pair up (Heat) or teams go all-out to obtain a superstar (Rockets), it has become commonly accepted that this is the best way to build a successful team. However, when well-balanced teams such as this year’s Nuggets showed us inequality might be overrated, I began wondering which is the better way to build a team. The truth is, there really is no right answer; there have been successful teams built with both symmetric and skewed distributions. However, by analyzing certain performance statistics, Win Shares and salary data from the past 11 NBA seasons, I tried to determine which approach was more reliable.

Read more after the jump

Basic Statistics

I wanted to find a way to measure this balance, or imbalance, and analyze whether it was to a team's benefit, or detriment. A perfect way to do this was using the statistic of standard deviation. Standard deviation measures spread from the mean, so if a team has a higher standard deviation of points, they had more spread out scoring. If they have a lower standard deviation of points, they had more balanced scoring. However, this measure is greatly influenced by the actual amount of points scored. Teams with who score more points are naturally going to have a higher standard deviation of points. To control for this, I divided each players scoring total by the total number of team points, resulting in a percentage that estimates each player's share of the team’s points scored in a given season. I then took the standard deviation of those percentages, resulting in the Adjusted standard deviation of points (Adjusted SD). A team with a higher Adjusted SD of points had a majority of its points come from a few players, while a team with a lower Adjusted SD of points had a balanced scoring attack. The same obviously applies for assists, rebounds, 3-pointers, steals, blocks, and turnovers.

           

It turns out that the way these stats are distributed amongst the team has a significant impact on the season total of the stat, the team’s offensive rating (Points/100 possessions), and their overall winning percentage. Using single regressions, I found that having more spread out scoring contributions (a higher Adjusted SD of points) leads to more points overall, a higher offensive rating and more wins. The same applies to assists, rebounds, steals and blocks. In other words, uneven assists (or rebounds, etc) leads to more overall assists, a higher offensive rating, and more wins. The same logic even applies to turnovers, as having a wide spread of turnovers leads to more on court success, probably because you want your turnovers limited to your primary ball handler. Interestingly enough, unlike all the previous stats mentioned, having a wider spread of turnovers doesn’t predict having more overall turnovers.

This is the same thing Nima Shaahinfar found in his analysis, summarized here. His results differed from mine in that he found that rebounds should be evenly dispersed amongst a team, as it creates a more efficient offensive and defensive unit. Shaahinfar reasoned this means better offense and defense arecreated if everybody crashes the boards. He used lineup statistics, whereas I used season aggregate data. He accounted for the stats of players in relation to the lineup they played with, while I used season totals, combining all of a team’s lineups into one data set. My method may be less precise, but I still feel that looking at how a team’s points, rebounds, etc, were allocated across an entire season is a valuable exercise and the results still have significance.

Using my data in multiple regression models yielded some note-worthy results. The most interesting one is displayed below in regression model 1, predicting offensive rating. The model predicts that, while controlling for how well a team shoots overall (FG%) and how well it shoots the three (3P%), the spread of those 3-point shot attempts and overall shot attempts is significant. With a positive coefficient on Adjusted SD of Field Goal Attempts and a negative coefficient on Adjusted SD of 3-Point Attempts, the model tell us that teams benefit from an uneven distribution of overall shots, but an even distribution of 3-point attempts. This shows us two things. First, successful offensive teams have many guys taking threes. Second, taking the Adjusted SD of 3PA into account, the fact FGA should be unbalanced means that our 2-point shots should have an uneven distribution as well. These results coincide with Shaahinfar’s results displayed in his blog.

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  Note: the analysis here is kind of tricky. If you have a lower Adjusted SD of 3PA, or a more even distribution of people shooting threes, you probably have just more guys who can shoot threes. Every team has at least three players who can hit a three, but when your guys 5, 6, and 7 are shooting threes, they are shooting them for a reason; they’re probably good at them. So it makes sense that a team with a low Adjusted SD of 3PA has a higher Offensive Rating, they have a lot of guys who can hit threes. The practical advice: load up on effective players who can shoot threes.

All of these results must be taken with a grain of salt. For example, the Warriors this past season had the most spread out 3PA (highest Adjusted SD of 3PA) mostly due to the Splash Brothers. The numbers show that offensive efficiency increases when those measures are lower, but this wasn’t the case for the Warriors and no team is going to pass up on Reggie Miller 2.0 and Reggie Miller 2.5 to keep their spreads as even as possible. The point is, over the past eleven seasons, the trend is that better offensive teams have had a balanced 3-point attack, but clearly every team has their own formula.

An interesting way to look at this concept of balance and imbalance is shot attempts and points. I ran single regressions on both the Adjusted SD of Field Goals Attempted and points. The models predicted an increase in the adjusted spread of both shots taken and points scored is better for your team. Did this hold true this past season? Well, listed below are the top and bottom 10 teams in spreading out both shot attempts and points. As you can see, this is an indicator of success.

 

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Win Shares

Another interesting stat to look at is Win Shares, basketball’s version of Wins Above Replacement(WAR). Win Shares relies heavily on Offensive and Defensive ratings. Developed by Dean Oliver, Offensive and Defensive ratings mostly use basic box score statisticssuch as PTS, FTA, REB, 3PM, AST, STL, BLK, and TOV as the inputs. By encompassing how effective a player was on the offensive and defensive end, a Win Share essentially represents the number of wins contributed by a given player. The sum of all the Win Shares on a team results in a number very close to their actual win total. So, by dividing each Win Share by the total number of Win Shares, the resulting percentages represent the proportion of a single win that can be credited to a given player. Looking at the standard deviation of these percentages (Adjusted SD of Win Shares) shows the distribution of a team’s overall contributions. How spread out or balanced does a team want its players’ individual impacts to be? Interestingly enough, it is better to be as balanced as possible. When put into a regression model while controlling for previous winning percentage, the Adjusted SD of Win Shares had a negative coefficient, meaning more spread out Win Shares is damaging for a team. In regression 2 below, I added in the Adjusted SD of specific stats that are part of the Win Share formula and modeled the non-independence amongst teams by including a random intercept. The results are similar.

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-->I also performed a similar regression, but added in the totals of all the individual stats as well (total points, assists, etc) and the adjusted SD of these stats all remained significant. This tell us that both the quantity and distribution of these stats are important.

Wait how does this make sense? You want more spread-out scoring, passing, rebounding and defending statistics, yet a balanced distribution of Win Shares, a statistic that is essentially a direct measure of a players scoring, passing, rebounding and defending? Yes, this is true and it stresses an important point. Players must have roles. Successful teams have players playing to their strengths in certain areas. They have players who score, other players who rebound, others who assist; all ideally contributing to a balanced distribution of Win Shares.

The takeaway here is that the public perception of players is skewed towards individual stats over team impact. I claimed earlier that an uneven dispersion of points, or having a player dominate the scoring, predicted more overall scoring. However, the correct way the phrase it is that having a player whose role is to score leads to a more effective offense. The same goes for having a player whose role it is to pass and get more assists, etc.

Other Measures

Want one more way to look at balance on a team? How about the allocation of minutes and funds? The results from a multiple regression predicting winning percentage from the standard deviation of minutes and Adjusted SD of Salary (because some teams differ a lot in their overall salary) are below.

 

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When regressing against winning percentage while controlling for previous year’s success, the spread of a team’s payroll has a positive relationship. Essentially, more successful teams have an unbalanced distribution of their salaries. The same results were found for minutes played; a more uneven allocation of minutes predicts more success. The reasoning behind regression 3’s results are much more obvious. If a team is evenly distributing its minutes and not having just a few players play the majority of the time, it probably doesn’t have any great players. And if a team’s salary is very balanced, it probably doesn’t have a great player on the team who is worth a big contract.

Applying this same logic to the previously mentioned measures of spread like points and rebounds, it makes sense why bad teams tend to have more balanced scoring. They have nobody who can score in bunches and differentiate himself from the rest of the pack. That’s why teams pay a premium for production, why one-dimensional scorers like Carmelo Anthony get max-deals, and even why irrational scorers like Michael Beasley, JR Smith, and Jamal Crawford have a place in this league (Thank God). But what we learned earlier with the application of Win Shares into the equation is that if you have a scorer, leave him to scoring. Surround him with other guys who can defend, rebound, pass and hit threes. Everybody else should ideally contribute an even share of these other statistics.

Conclusion

I started this post talking about balance. Do NBA teams really want balance? Well the answer is yes, in some respects, and no, in others. It depends on your team. An optimal roster should have unbalanced salaries, but this doesn’t mean all players shouldn’t contribute. Successful teams have had a more even distribution of Win Shares, meaning they receive significant contributions from everyone. You want players with specific roles, and players who know those roles. Does that mean you don’t want a player like LeBron James, who can lead his team in rebounds, points and assists and any given night? Of course not, he’s the best player in the league. No team isn’t going to sign LeBron because he will skew their allocation of Win Shares. Certain players tend to ruin all the analytics done in the NBA. They usually have LeBron or Durant in their name. Thanks a lot guys.

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

Also check out part one!

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

Event Recap: Steve Seiferheld ('98)

On April 9^th 2013, the ILR Sports Business Society was fortunate enough to host alumni Steve Seiferheld '98, Senior Vice President of Turnkey Intelligence. Turnkey Intelligence is the sports and entertainment industry’s leader in consumer insights collected from surveys. Seiferheld graduated from Cornell with a Masters in Statistics. In his time at Turnkey, he has worked with WWE, ESPN, NFL, Disney on Ice, and 18 out of the 30 MLB teams, in addition to many others. During his Skype interview, he reviewed professional career and offered advice to members of our club in how to get a job in sports. Read about it after the jump.

Seiferheld has a huge client base that spans all four major sports in the United States. Before he earned his job with Turnkey, his first job out of college was with Nabisco, where he helped drive consumer research for product development. Up until a few years ago, he was working with the Home Shopping Network. He reminded members in the audience that there are many ways to get into the world of sports and that sometimes you have to take detours to get there.

Seiferheld then discussed his job at Turnkey, which deals with market research with a sports focus. He explained that from the business side of sports, every fan is a consumer. However, not all consumers are fans. He explained his point further by posing the question “How does a sports team losing affect my life in any tangible way?” After a brief moment he responded that it should not, but it still does and that is what makes people fans.

Seiferheld’s job is based on discipline of market research which he defined as “doing research on your market.” If you’re selling something and you want to understand buyers better you need to do your research, however, there are a lot of different ways to go about it. This is where Turnkey steps in. One tactic they use is to focus on primary research - custom feedback from the clients. This can be done by surveys or conversations. Research is done on site and off site, depending on the type of surveying that is required. Turnkey is not always asked to analyze the data they collect either, sometimes the job is solely to collect it.

While explaining the importance of ratings he gave the WNBA as an example. While most people wonder why ESPN would pay the league $12 million a year, he explained that it was a brilliant move. During the WNBA season, baseball is the only other sport on television. If there is no baseball game, then there is nothing else to put on the station. The average WNBA game also garners between 300,000-350,000 viewers per game, so while it is only used as “filler,” it still fares better than most other shows. He gave the Florida Panthers of the National Hockey League as an example. They are a professional male sports team. Yet they only garner around 4,000-5,000 viewers per game.

Audience Question: 

“Do you ever have conflict of interest with places and companies you work for?”

Answer: 

He has a confidentiality agreement, so he cannot share information with a rival team or organization. He can, however, when looking at customer satisfaction for a team, let them know whether they are on the high end or low end of the spectrum. He is not allowed to give specifics, though.

Seiferheld then took some time to critique some of his clients. One issue occurs when people ask him to do a survey without knowing what is wrong and why they are asking for the data to be collected. He explained that it is not worth anyone’s time if you are asking for a survey to be done just for the sake of being done. He also criticized sports teams who sometimes devote too much money to the on-field business, but not enough goes into the off-field portions. In his opinion, if more teams put money into these off-field portions, they could better the entire system and generate more revenue.

Jeremy Lin

When asked about the effect of Jeremy Lin last year in New York, Seiferheld explained how Linsanity single-handedly did things for the New York Knicks that Seiferheld could never do for any team. Even with all his expertise working with ticketing, sponsorships, and merchandise sales, outliers like Lin cannot be compared to. In his opinion, if Lin would have been in a smaller market, he would have still had an impact regardless, just a smaller impact.

Lottery Tickets

When talking about how fans are consumers, Seiferheld discussed how most states have lottery tickets that are co-sponsored with sports teams. He asked rhetorically, “Why does a sports fan want to buy this lottery ticket?” He did research on lottery tickets by asking people whether they agreed or disagreed with the statement “I don’t mind losing money as long as the money helps my team win.” The results showed that more people agree then disagree. Yet there is still not way that the money spent on the lottery ticket is affecting the team’s performance in any way. However, people still feel as if they are helping the team win by buying lottery tickets associated with the team.

Boston Red Sox

Seiferheld went into a discussion about a specific case study with the Red Sox after their poor 2011 season. There was also a controversy over whether players were eating fried chicken and drinking beer inside of the team clubhouse. Turnkey was contacted to see if the Red Sox brand had been tarnished. Fans were surveyed on how they felt when they walked into Fenway. After collecting the results, which included words such as “heaven” and “museum,” and analyzing the data, Turnkey was able to decide that the Red Sox had nothing to worry about. Their image remained untarnished despite a bad season and small controversy.

Other Case Studies

Marathons: Event planners wanted evidence regarding how much money marathons bring into cities. This evidence is necessary because of the huge sums of money that events such as marathons demand from the city such as police officers, and areas to store stuff for the runners. Based off of data from hotels, a lot of money is made; this also gives hotels opportunities to sponsor marathons. The end results proved that marathons make a lot of money for cities.

College Sports: A college was trying to make the jump from Division 1AA to Division 1A. Research had to be done to be sure that the basic requirements to make the jump were met. The main requirement was that the attendance threshold of 15,000 students had to be met. After research, it was shown that more than 15,000 students regularly attended games. Moving up a division would also mean new, better opponents which would inherently mean better attendance from fans. Seiferheld worked directly with the athletic director on this case.

     Advice

To finish the event, Seiferheld gave advice to the members of the club on how to enter the sports industry.

-A pedigree in law, data marketing, statistics, and other attributes lead to being a better person to hire

-Read the Sports Business Journal

-Be willing to do anything to get into the industry; if you have to sort mail, sort mail

-Business majors get more preference than sports management majors

-Meet as many people in the industry as you can—connections are key!

We would like to thank Mr. Seiferheld for taking time out of his busy schedule to Skype with members of ILRSBS. His advice and his knowledge were truly insightful. His kind demeanor was also appreciated, as were his funny stories such as his signature being inside of the Green Monster in Fenway Park between Tom Petty’s and Bruce Springsteen’s. We hope to host him again in the future!