Independent Variables:

Because their objectives are different, I create different independent variables for the game- and team-based fans. Game-based fans have portfolios of different forwards; therefore understanding strategies that enable them to create and manage ‘winning’ portfolios is of value (and interest). Stated in terms of the dependent variable, the goal is to identify and understand factors that enable game-based fans to have a better rank (rank 1 being the best). Helping the game-based fans meet their objectives consists of two components: (1) managing the risk in their portfolios and (2) minimizing their costs. For team-based fans I seek to identify factors that minimize costs. I begin with the cost-minimization variables that affect both fan types.

Cost minimization strategies:

One strategy that fans could use to minimize their cost is to time their purchase (and sale). The operationalization of the corresponding independent variables are discussed below:

(a) Purchase Timing: A cross-tabulation reveals that on average fans spend $60 to buy a forward if they buy early versus $138 if they buy late. Buying early can therefore help the fan by minimizing her cost30. If the forward was bought in the pre-season or

within the first 10 weeks of the regular season it is classified as an early buy (if the forward was bought after week 10 and into the tournament season it is classified as a late buy). Around the end of the first 10 weeks, the new year sets in and a newer, more frenzied chapter in the season begins. Therefore I use the 10 week mark as the natural division between early and late buys.

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While it can be argued that buying late allows fans to have more information which may guide their buying choice better, it can be counter argued that even if they bought the forward early fans could always sell the forward based on the changing team performance (new information).

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(b) Sale Timing: Resale, per se, helps increase the fans’ ‘net dollars’. Further selling early helps the fan minimize her risk. Whereas selling late, on average, commands a higher price ($153 versus $130) if the team performs well, there is a risk associated with waiting for too long. If the team is performing badly the fan may not be able to recoup the amount she paid to buy the forward. Worse still, if the fan waits till the tournament begins, the team may not make it past a certain round and the forward becomes worthless instantaneously. If the forward is resold in the pre-season or within the first 10 weeks of the regular season, it is classified as an early sale (forwards that are resold later are classified as late sales).

(c) Type of purchase (Issuance versus Trade): The average sellers’ price per forward is higher for a trade ($172) than an issuance ($97). This reveals that the firm is pricing more conservatively compared to what the market is willing to bear. From a fan’s perspective buying an issuance seems to be a cost minimization strategy. I create a ‘trade type’ variable that is coded ‘1’ or ‘0’ depending on whether or not the transaction is a trade or an issuance respectively.

Extant literature in finance offers some insights on managing individual investors’ portfolio risk. We use these insights to motivate game-based fans’ risk-management strategies.

Risk-management strategies:

(d) Portfolio Breadth:

i. Linear effects: In line with the existing literature in finance (Markowitz 1952), diversifying a game-based fan’s portfolio is hypothesized to be a better risk-management strategy. In this setting a diversified portfolio amounts to having many unique teams’ forwards. As the number of unique teams in a

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fan’s portfolio increases, this increases the fans’ chances of attending the game. For each bidder, I create a measure of portfolio breadth: a count of the total number of unique teams that the fan held in her portfolio till the end of the regular season. On average game-based fans held about 4 teams in their portfolio till the very end. Team-based fans, by definition, have a single team in their portfolio. Therefore even though they could be left with zero or one team at the end of the regular season, adding this variable to the team-based fan analysis is of limited value. This is because the recommendation of having more (or less) unique teams does not apply to team-based fans who have a single team in their portfolio.

ii. Non-linear effects: As the number of unique teams increase so does the cost associated with the purchase of multiple fan forwards. As hypothesized in the finance literature (Berger and Ofek 1995, Kennon 2007), beyond a certain threshold the incremental value of an additional portfolio investment (buying an additional team, in this case) is lesser than the cost involved. To capture this effect for game-based fans I introduce a quadratic term that represents the square of the number of unique teams a game-based fan possesses. As with the linear case, this variable is not applicable for the team-based fan analysis. (e) Portfolio Risk: As the number of unique teams in the portfolio increases, the

variance of teams in terms of their rankings also increases. Consistent with the wisdom in portfolio management (Stein and DeMuth 2008) as the portfolio risk increases it has a negative effect on the fans’ chances of attending the game. I create a “spread” variable that captures the difference in ranking at the end of the regular

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season between the highest and lowest ranked teams in each game-based fan’s portfolio. In other words the spread captures the gap in the best and worst teams’ performance in a fan’s portfolio. If the portfolio has better ranked teams the spread measure is adjusted (and renamed as ‘portfolio risk’) to reflect this. The portfolio risk measure is created to reflect the differences between portfolios with the same gap (the gap between teams with rankings 1 and 11 is the same as the gap between teams ranked 21 and 31; in the first case the portfolio risk is lower than in the second). Because the team-based fan has a single team in her portfolio, the portfolio risk variable is not applicable for team-based fans.

(f) Team Performance (‘Rank Deviation’): Investors have been cautioned against chasing recent performers (Stein and DeMuth 2008). In our context, I check to see if buying a team that is coming off a recent win can be a better strategy to pick the right teams (than buying one that is coming off a loss). In other words I check the

effectiveness of the strategy of buying a team that at week t is performing better

relative to its performance at week t-1. I create a ‘rank deviation’ variable which is the difference in team ranking at the week of purchase from that in the previous week. If the rank deviation is positive it implies the team is performing worse in the current week. Though this variable is classified as capturing a risk-minimization strategy it is plausible that this strategy can be used to minimize costs as well. So I use this

variable in the team-based fan analysis as well.

(g) Expert Predictions (‘Pre-season picks’ and ‘Mid-season picks’): Finally, a recommended risk-mitigation strategy for individual investors involves composing the majority of their portfolio with stocks from reputed firms (Stein and DeMuth

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2008). To draw a parallel to the sports teams context I obtained the ‘top 5 winningest teams of all time’ from the NCAA website (Johnson 2007). At least in the last few years (since 2004) it seems experts have picked most of their recommendations for the Final Four teams from this list: Duke, Kansas, Kentucky, UCLA, UNC – all storied teams in college basketball history. So in this context the hypothesis is that fans would be increasing their chances of attending the game by heeding expert predictions.

Expert predictions are made twice during the season – once before the season begins (week 1) and the other about mid way through the season (week 11). Pre- season picks reflect experts’ predictions before the teams start playing in the regular season. These predictions are based on how the teams performed in the previous season. Mid-season picks reflect an updating by experts; depending on the teams’ performance in the current season they make revisions to their pre-season forecasts. I hypothesize that both pre- and mid-season picks help fans make the purchases on the ‘right’ teams – the mid-season picks possibly help the fan more than pre-season picks (in terms of coefficient size). I obtained expert predictions from the Entertainment and Sports Programming Network (ESPN) website’s Coaches Poll during the 2005- 2006 season. The week 1 predictions were labeled “pre-season picks” and their week 11 predictions were labeled “mid-season picks”31.

Because heeding expert predictions could be used as a strategy to reduce losses, this strategy also has cost implications. Therefore I use this variable in the team-based fan analysis as well.

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The pre-season favorites were Duke, Villanova, UConn, Mich. St. and Texas. The mid-season favorites were UConn, Duke, Memphis, Florida and Texas.

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