Econometrics paper, we try to quantify the relative importance

Econometrics Term Paper

Valuation of Bowlers in the Indian Premier League Auction- Quantifying weightage of significant characteristics
Objectives of the Study
In this term paper, we try to quantify the relative importance of various bowling attributes, and non-playing attributes in determining the sale price of a bowler in Indian Premier Leagye 2018.

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The Indian Premier League (IPL) is a professional Twenty20 cricket league in India contested during April and May of every year by teams representing Indian cities. The league was founded by the Board of Control for Cricket in India (BCCI) in 2007.

A team in the IPL can acquire players through any of the three ways: the annual player auction, trading players with other teams during the trading windows, and retaining past players. The Indian Premier League introduced an auction for cricketers for the first time in the history of the sport. Due to the auction and bidding process monetary values were associated with cricket players for the first time. This created an open market place to assess a cricket player’s worth. In this paper, we will be taking data from the IPL 2018 auction to be held in Bengaluru on January 27-28, 2018. A big pool of 578 players will be going under the hammer in this auction.
The Indian Premier League divides the players into three categories according to their expertise in the game- Batsman, Bowlers and Wicketkeepers. In this paper, we will be focusing on the subsection of Bowlers. Approximately 200 bowlers will go under the hammer in the IPL 2018 auction.
This paper seeks to determine what characteristics determine the monetary values assigned to bowlers by teams in IPL. Given the data on final bid prices, retention prices, cricketing attributes of bowlers, non- cricketing attributes and other relevant information, we try to understand which attributes seem to be important in deciding the final price of bowlers.

Literature Review
Rastogi and Deodhar (2009) employed the bid and offer curve concept of hedonic price analysis and econometrically established a relation between the IPL-2008 final bid prices and the player attributes. Hedonic Price Analysis is based on the hypothesis that a good/service can be treated as a collection of attributes that differentiates it from other goods/services. Extending the analogy to cricket, a cricket player is valued for his on-the-field (and perhaps, off-the-field) performance. They propose that a cricket player sells his cricketing services for the IPL tournament. The franchisee team owners bid for the player services, for team owners would like to maximize their utility (chances of winning and maximizing profit), and, player performance is an important argument of their utility function. Therefore, given the data on values of various attributes of cricket players and their final bid prices, one can estimate the following hedonic price equation econometrically,
Pi = g ( zi1, …,zij, …, zin),
where Pi is the final bid price paid to a cricketer i for the IPL tournament and zij is the value of the attribute j of the cricket player i. The hedonic price equation, in this context, is a locus of equilibrium final bid prices and player attributes.
Karnik A. (2010) also estimates the value of cricketers using hedonic price models. It is done on the basis of prices of bids the players received in the IPL 2008 auction. Selecting the independent variables was a difficult task since before IPL 2008 auctions, the numbers of T20 matches played were very few in number but players selected in the auction were tasked only to play T20 matches. It followed the Extreme Bounds Analysis approach where the regressors which were to be included in hedonic pricing model equation were narrowed down to a few and chosen based on the underlying model. The underlying model was simply taken as the player’s performance. It inferred the player’s performance from the number of runs scored and the number of wickets taken. These two variables have been taken as regressors. Karnik finds that in addition to variables like runs scored and number of wickets taken, variables like the age of a player, home player bias and the rate at which runs are scored by a batsman (strike rate) are important as well.
Lenten, Geerling and Laszlo employ a range of cross-sectional models with a view to establish the factors that determine the valuation of professional athletes in a highly-specialized sport, with an application to cricket’s Indian Premier League. Their data set comprises the 80 players for whom the bidding price resulted in a ‘sale’. They considered a large number of explanatory variables, ie. 57. The explanatory variables were categorized into Identifiable Characteristics and Career Statistics, which were broken down further to give a detailed view of the variables. The model considered for estimation was a log – linear model. While Karnik (2010) performs a similar exercise via extreme bounds analysis (EBA), they use an alternative methodology. The models formulated were able to explain over 60 percent of the variation in price. Here again, it was found that Indian players commanded a premium price suggesting a home player bias in the auction. Interestingly for bowlers, bowling average (number of runs conceded per wicket taken) was found to be negatively related but the bowling strike rate (number of balls bowled per wicket taken) had a positive effect implying that economical bowlers are placed higher. A tendency to overbid in the first rounds of the auction and underbid in the middle round followed by overbidding in the last round was also found indicating the presence of winner’s curse phenomenon.
In Parker, Burns and Natarajan (2008) they had the objectives of analyzing: The extent to which player valuations are based on performance, and which performance features matter? Whether, and how much, the rules set during the auction affected player valuations? In particular, do the rules impose an artificial scarcity of Indian players, in particular young Indian players, through the cap on the number of overseas players and the requirement that there be a certain number of young Indian players in each squad. They explore the determinants of valuations and employ a reduced form model with standard OLS techniques to run their regressions. The explanatory variables employed cover a variety of performance factors (such as batting and bowling averages) in different forms of cricket, experience in different forms of cricket, and characteristics of players (such as age and nationality). The dataset pertains only to the 2008 IPL auction.
Marwaha D. Y. (2013) attempted an estimation of the monetary values of the players in the IPL auction and used this to determine which factors, abilities and skill set were highly valued by the teams. The sample taken was of all the IPL auctions till 2013, taken together from the first auction in 2008 to the 2013 IPL auction. To control for time, the paper introduces time variables in the model to account for the different years. The dependent variable is taken both as the base price (reserve price) and the bidding price (sale price). An interesting aspect of the paper is its treatment of the runs scored and the wickets taken variable, where it takes them as the ratio of the runs scored (wickets taken) by a player in the T20 format to the total runs scored (wickets taken) by all the players in the same format. The reduced form regression results show that taking the dependent variable as bidding price rather than base price was able to explain a lot more variation in the model and also explain the statistical significance of the runs scored and wickets taken factors.
Boorah and Mangan observe that the IPL 2008 auction was characterized with large and unexpected base–final price differences. This raises the possibility that the bidding process was driven by the ”irrational exuberance” created within the newly formed franchises. Their article measures the scale of such exuberance and evaluates, on the basis of their IPL record, whether players were good or bad buys. Their paper explores a number of factors common to the competitive bidding process in professional sports. These include overconfidence, winners curse, and false consensus.


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