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ANALYTICS Improved Analytics to Find Undervalued Players for Your Fantasy Basketball League

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ANALYTICS Improved Analytics to Find Undervalued Players for Your Fantasy Basketball League FEATURE ANALYTICS Slam Dunk Improved analytics to find undervalued players for your fantasy basketball league | by George Recck, I. Elaine Allen, Adam Kershner, Zachary Mittelmark and Julia E. Seaman 42 QP February 2019 ❘ qualityprogress.com Slam Just the Facts Fantasy basket- ball leagues are becoming more popular, and rank- ings and analytics are an important part of managing a team. To identify the best players to select for your fantasy team, a new rating system has been developed. This player valuation model examines how much an individual player contributes to a team’s overall field goal per- centage and free throw percentage. qualityprogress.com ❘ February 2019 QP 43 FEATURE ANALYTICS In the past 30 years, we have entered the world of big data. TABLE 1 The magnitude and accessibility of data—and the ability to analyze those data—have advanced dramatically. This advance- ment of data processing power can be seen in diverse fields, Fantasy league points awarded including investing, web analytics and sports metrics. Following this trend, fantasy sports have significantly devel- for PTS and AST: 2016-2017 oped, both in terms of sophistication and popularity. Rankings Team PTS Points Team AST Points and analytics in sports leagues are everywhere and becoming SGFF 8,752 9 Mr. C 2,083 9 more important. One of the easiest places to see this is in fantasy sports because the data are public, and there are online SalesDog 8,719 8 SalesDog 2,044 8 business and betting opportunities. The difficulty in summariz- Mr. C 8,352 7 The Illini 1,954 7 ing this data for choosing players for fantasy sports teams lies The Illini 8,235 6 FengD 1,919 6 in identifying which metrics are best at discriminating between Crew 8,156 5 Crew 1,917 5 the highest quality players and the rest of those in the league. Viper 8,119 4 Viper 1,877 4 This article focuses on fantasy basketball, introducing a new method to estimate the overall quality of a given basketball Ghost 8,012 3 SGFF 1,792 3 player, contrasted in tandem with current procedures used by Cobra 7,658 2 Cobra 1,627 2 sports websites to estimate player value. This approach has led FengD 7,284 1 Ghost 1,207 1 a few of this article's authors to three championships and two AST = assists PTS = points scored second-place finishes in the last eight years in a mature fantasy basketball league. cap, or have position or play time requirements, to Fantasy basketball overview restrict and control team makeups. Most leagues Previously overshadowed by baseball and football, fantasy even allow trading to occur among GMs. basketball has been growing. In fact, fantasy basketball During the season, after the draft is completed, revenue surpassed that of fantasy football in 2016 at FanDuel, GMs set their starting lineups for the starting period. and it was projected to do so in 2017 at DraftKings. These two This can be done on a daily, weekly or monthly basis, sites represent the largest fantasy gambling outlets.1 In addi- or just once for the whole NBA season, depend- tion, a recent ruling by the U.S. Supreme Court gave states the ing on the fantasy league preferences. Thereafter, ability to authorize sports betting, which all but nine states statistics for the players in a GM’s starting lineup have now allowed.2 accumulate and are automatically ranked. The Many believe the rapid growth of fantasy basketball is due to overall goal is to earn the most points in the league sports fans’ desire to remain engaged with sports and fan- to win the season. tasy leagues during Major League Baseball’s off-season. Like Despite the potential to win cash, the return baseball, the National Basketball Association (NBA) has many on time invested is typically lower than minimum games played daily, many traditional as well as new statistical wage. Thus, participating in a fantasy league is done scoring measurements, and the advent of digital tracking to purely out of enthusiasm for the game and bragging provide endless amounts of data per game or player. In addi- rights. Many leagues even have intermediate prizes tion, data analysis opportunities are numerous: Most data in (monthly or daily), which can keep a team out of the basketball are larger in magnitude than other sports, and much race for first place engaged in the league. These are are continuous or pseudo-continuous in comparison to baseball. the typical eight categories involved in calculating While it varies by group and hosting site, anywhere from four an overall fantasy NBA team ranking: to 20 people can form a fantasy basketball league during the + Assists (AST). NBA season. The participants act as general managers (GM) + Blocks (BK). for their respective teams. These GMs meet to draft a roster of + Field goal percentage (FGP). players from the pool of NBA players. After a player is drafted, no + Free throw percentage (FTP). other GM can draft that player for his or her team. More recently, + Points scored (PTS). drafts are typically held online. Drafts may be held all at once, + Three-pointers made (3PT) over several days or even over several weeks. Many leagues + Total rebounds (TRB). place dollar values on players and have a corresponding salary + Steals (ST). 44 QP February 2019 ❘ qualityprogress.com Table 2 (p. 46) shows how the points earned in each ranked statistical category throughout the season are totaled to determine the league’s ending standings. It is not necessary for a GM to win the most points in every category, but rather have the General highest average value per ranked category. Ideally, the GM should aim to score a seven across the cate- Calculation gories for the best chance to win the league. Valuing players of Added A player’s value for fantasy leagues is based on his statistics in the ranking categories. In today’s mod- Value ern setting, actual NBA data are readily available online from sources such as CBS Sports, ESPN and Tables 4 and 5 show how to calculate this added Yahoo Sports, among others. Most of these sites value to FGP or FTP for an individual player. Below provide rankings for NBA players based on their are the equations to do so for any player where proprietary formulas that they promote for use in AvgFG and AvgFT represent the team’s average fantasy leagues. However, while these sites provide number of field goals and free throws multiplied by a generalized overview of the league, not all fantasy four to represent the other four players. AvgFGA and AvgFTA represent average attempts, while leagues measure the same categories, so rankings FB, FGA, FT and FTA represent those made by the may not be precisely tailored for a specific league. individual player being evaluated. We were interested in deciphering these rankings for two reasons: (4*AvgFG)+FG AvgFG 1. To determine their relevance to the eight FG Add = – (4*AvgFGA)+FGA AvgFGA fantasy scoring categories. 2. To evaluate the quality of current ranking (4*Avg FT)+FT Avg FT FT Add = – schemes and identify higher quality metrics (4*Avg FTA)+FTA Avg FTA for choosing players. For sake of brevity, all further analyses in this After applying this calculation to the top 50 play- article will use the CBS Sports rankings. ers, we can build the following regression models: + Predict FG Add based on FG made, FG missed and Multivariable modeling FG attempted Multivariable linear regression models are used + Predict FT Add based on FT made, FT missed and to evaluate the current ranking metrics using a FT attempted. separate model by year for nine NBA seasons. Each model calculates a coefficient for each of the eight metrics as predictors of the overall CBS rank. Using this method, each predictor’s coefficient controls for the effect of the other seven metrics, and its statis- Calculating league standings tical significance in the overall model is evaluated. Table 1 illustrates how league points were awarded for PTS and Both the size of the coefficient and its consistency AST categories based on the statistics accumulated over the over multiple years are important. 2016-2017 season of a mature fantasy NBA league. This model uses the last nine NBA seasons (from The table shows that regardless of whether a player wins a 2008-2009 to 2016-2017) based on the top 100 category by one or 1,000, only nine points are assigned to the players as ranked by CBS Sports. Table 3 (p. 47) category—thus, you need to win a category by just a single unit offers this nine-year history of regression coeffi- to accumulate nine points. You might think of this in the same cients for each statistical category and their way as winning a bid on a contract: A business bidding for ser- relative strength. vices would want to offer just enough to beat its competitors, The strength of a category can be measured by without sacrificing too much of its own resources. the size (weight) of the regression coefficients. qualityprogress.com ❘ February 2019 QP 45 FEATURE ANALYTICS TABLE 2 Looking only at the individual regres- sion coefficients and p-values for the covariates for the 2016-2017 season (the Final fantasy league standings: 2016-2017 last column), we can use these coeffi- Rank Team 3PT AST BK FGP FTP PTS ST TRB Total cients to form the ranking model 1st Illini 3 8 7 9 3 6 6 8 50 that follows. The regression equation for 2016-2017 2nd SalesDog 9 3 8 5 2 8 5 3 43 to predict CBS rank is: rd 3 Viper 8 6 4 3 9 4 3 5 42 CBS Sports rank = 212.6 + 0.22(3PT) 3rd Cobra 7 5 2 8 8 2 8 2 42 + 0.03(TRB) + 0.04(AST) + 0.29(ST) 5th Mr.
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