In this Project Post I will be reviewing past research papers from SSAC’16 and making comments on them (in red) in order to help me come closer to an original topic of my own, but also figuring out the depth in which my project needs to be in, in order for it to be commendable.

#1

The Pressing Game: Optimal Defensive Disruption in Soccer

By: Iavor Bojinov and Luke Bornn
ABSTRACT:
“Soccer, the most watched sport in the world, is a dynamic game where a team’s success relies on both team strategy and individual player contributions. Passing is a cardinal soccer skill and a key factor in strategy development; it helps the team to keep the ball in its possession, move it across the field, and outmaneuver the opposing team in order to score a goal. From a defensive perspective, however, it is just as important to stop passes from happening, thereby disrupting the opposing team’s flow of play. Our main contribution utilizes this fundamental observation to define and learn a spatial map of each team’s defensive weaknesses and strengths. Moreover, as a byproduct of this approach we also obtain a team specific offensive control surface, which describes a team’s ability to retain possession in different regions of the field. Our results can be used to distinguish between different defensive strategies, such as pressing high up the field or sitting back, as well as specific player contributions and the impact of a manager.”

-Basically these guys used cartography and was able to map out where each team in the English Premier league had strong and weak points in their passing

-They derived each variable and each equation to create graphics for coaches to use

-The objective of their project is very clear, I honestly had a hard time understanding their equations from just glancing at it, but I’d like to know how they “started from scratch” to determine this equation and how they considered the parameters

-This project gave me the inspiration to create a novel calculating tool for the sport, however because soccer is a more continuous sport (See Dr. Bobby Vasari) and it’s not one I am that huge of a fan of I’m probably not going to do one that is similar

#2

“The Thin Edge of the Wedge”: Accurately Predicting Shot Outcomes in Tennis using Style and Context Priors

By: Xinyu Wei, Patrick Lucey, Stuart Morgan, Machar Reid and Sridha Sridharan
ABSTRACT:
“The aim of this paper is to discover patterns of player movement and ball striking (short-and longterm shots, and shot combinations) in tennis using HawkEye data which are indicative of changing the probability of winning a point. This is a challenging task because: i) behavior can be unpredictable, ii) the environment is dynamic and the output state-space is large and iii) examples of specific interactions between agents may be limited or non-existent (player A may not have interacted with player B). However, by using a dictionary of discriminative patterns of player behavior, we can form a representation of a player’s style, which is interpretable latent factors that allows us to personalize interactions between players based on the match context (opponent, matchscore). This approach allows us to perform superior point predictions, and to understand how points are won by systematically creating and exploiting spatiotemporal dominance.”

-This project deals with tennis and it’s actually quite related to this year’s hackathon theme about making a sport human

-The objective is not as clear, but from my understanding they are recording and trying to find patterns in professional tennis player’s movements in order to determine each player’s distinct style. Once the style is determined, the opponent then will have a profile of the player and perhaps will be ever to play better against the “exposed” player knowing their habits

-In their conclusion they stated that this could expand to basketball or soccer, I think I’d have to know a little more about the Hawkeye technology they used for this paper, however this is an interesting topic and perhaps I can expand on how to determine the style of a basketball player using other technologies. 

#3

Using Digital Signals To Measure Audience Brand Engagement At Major Sports Events: The 2015 MLB Season

By: Peter Ibarra and Peter E. Lenz
ABSTRACT:
“This research tests the hypothesis that digital data signals gathered from MLB stadium visitors can provide significant insight on the value of brand sponsorships and also inform on optimization of brand placement. The digital data signals analyzed in this study include mobile device location data collected from all MLB stadiums during the 2015 year and the online browsing behavior associated with these devices. The audience research is unique as it marries offline behavior with online behavior by using online behavioral data to inform on visitors to a physical location. Results from our study show that brand sponsorships do have an impact on fan engagement, and in some cases increase in value as the season progresses. As an example, Colorado Rockies fans at Coors’ Field are 13.6 times more likely to visit the airline sponsor’s website than the general population and 4.1 times more likely than all other MLB team fans. If we look at monthly trends, the propensity for fans to visit the airline sponsor site versus all other MLB fans increased from 4.1 to 6.8. This research has far reaching implications for matching teams with prospective sponsors while at the same time providing a comprehensive perspective on how the audience at each ballpark is interacting with current brands. Furthermore, the methods described allow one to quantify the impact of corporate sponsorship in a way that has never been explored before. Lastly, the techniques described here are not limited to MLB parks or to web site visit behavior.”

-This project wasn’t as directly related to sports, it was more a sports marketing and communication job

-Honestly, I wasn’t as interested in this project so I didn’t read it as closely, I do think it was cool how they determined how each stadium was receptive to different industries of brands

#4 (I actually skipped the 4th one on the website because I didn’t want to read another marketing one, I’m more interested in reading about the research papers that directly optimize the sport itself, not the business surrounding it

Recognizing and Analyzing Ball Screen Defense in the NBA

By: Avery McIntyre , Joel Brooks , John Guttag , and Jenna Wiens
ABSTRACT:
“Finding an effective counter to the ball screen is a high priority for NBA coaching staffs. I n this paper, we present construction and application of a tool for automatically recognizing common defensive counters to ball screens. Using player tracking data and supervised machine learning techniques, we learn a classifier that labels ball screens by how they were defended. Applied to a selection of games over four seasons, our classifier identified and labeled 270,823 attempts to defend a ball screen. At the team level, we identify outliers who favored a particular defensive scheme on the way to successful seasons. F or example, the ’12-13’ Bulls went “over” 7% more often than the average team that year. For players, we examine both offense and defense. Offensively, we report how often players face a given defense and their effectiveness in creating points from those situations. Notably, Damian Lillard sees defenders go over in ⅔ of his screens, but with 0.84 pts/poss he’s among the league’s best at capitalizing on these opportunities. Defensively, we examine pairs of players and their ability to stifle opponent scoring. In ’13-14’, Dwight Howard and Jeremy Lin were particularly effective when Lin went over screens, holding the offense to just 0.27 pts/poss. This fully automated tool opens the door to analysis of defensive tactics at an unprecedented scale.”

-This paper was right up my alley in terms of interest, however they used a tool called STATS SportVU system. 

-I’ll have to look into more of how this technology works in order to deeply understand how their data was collected but I like how they calculated the net loss or win if you go over screens.

-I was reminded that especially when it comes to basketball it must not be in jargon, and I thought the media they used to indicate a trap or a pick and roll play was appropriate.

#5

Accounting for Complementary Skill Sets When Evaluating NBA Players’ Values to a Specific Team

By: Joseph Kuehn
ABSTRACT:
“This paper develops a player evaluation framework that stresses the importance of accounting for complementarities between teammates when evaluating players. This is done by developing a probabilistic model of a basketball possession as a progression of events, where the probability of each event’s occurrence is determined by the offensive players’ skills, the defensive players’ skills, and the complementarities between the skills of teammates. Evaluating players using this framework allows me to assess the substitutability between different game actions, the lineup-speciϐic value a player brings to a team, and the players that are the best and worst teammates. It also allows me to separately identify the individual effect from the effect teammates have on a player’s statistical production, and to evaluate whether player complementarities are valued in the market for NBA players in terms of higher salaries. I ϐind that complementarities are under-valued, and that players are instead paid mainly for their individual statistical production.”

-First off this is the first project I’ve seen that was done solo so it definitely gives me hope

-I also like the fact that this author took a myth from MLB and applied it to NBA to “bust it,” many sports have myths that are overarching

-The objective on WHY this was calculated or the application is not as clear, I’d like to see exactly who can use this data and how, however I like that the projects underlying theme is an optimistic one that teamwork is important and “everyone needs to do their part”

-Later in the “meat” of the paper the author does detail how it can be applicable to players adding this to their profile when they are a free agent and it’s cool that he figured out the statistical output of how much a player adds to the team and then how much the team adds to him

-I guess the greatest difficulty is really the model and figuring out HOW to make what numbers represent what and making a model “fair”

-Another important note to make is that this author just used SI.com as a source to watch the play by plays and that resource is readily available to everyone.

—–I stopped here because the rest don’t pertain to the NBA which is the sport I’ve decided on evaluating with my paper

 

 

 

 

 

 

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