The behavior of informed traders, or “smart money,” in sports betting markets has long been of interest to researchers. In this paper, we focus specifically on the behavior of smart money in Major League Baseball (MLB) daily fantasy sports (DFS) contests to determine whether they avoid cognitive-behavioral biases to increase their expected earnings. Specifically, we investigate whether smart money avoids the hot hand bias, where individuals tend to overestimate the likelihood of success for players on a hot streak. Using a dataset of MLB DFS contests, we find that winning lineups have lower usage rates for players exhibiting the hot hand compared to losing lineups. This suggests that smart money identifies and fades the hot hand strategy to increase their expected earnings.
AliA.KlasaS.LiO. Z. (2008). Institutional stakeholdings and better-informed traders at earnings announcements. Journal of Accounting and Economics, 46(1), 47–61. https://doi.org/10.1016/j.jacceco.2008.06.001
CabotA. N.MillerK. C. (2011). The law of gambling and regulated gaming: Cases and materials. Carolina Academic Press.
4.
CamererC. F. (1989). Does the basketball market believe in the “hot hand”?The American Economic Review, 79(5), 1257–1261.
5.
DuganZ.GreysermanA. (2020). Skew and trend aversion: The impact of positive skew and behavioral biases on allocation decisions. The Journal of Alternative Investments, 22(3), 78–94. https://doi.org/10.3905/jai.2019.22.3.078
6.
EasleyD.O’HaraM.SrinivasP. (1998). Option volume and stock prices: Evidence on where informed traders trade. The Journal of Finance, 53(2), 431–465. https://doi.org/10.1111/0022-1082.194060
GandarJ. M.DareW. H.BrownC. R.ZuberR. A. (1998). Informed traders and price variations in the betting market for professional basketball games. The Journal of Finance, 53(1), 385–401. https://doi.org/10.1111/0022-1082.155346
10.
GandarJ. M.ZuberR. A.DareW. H. (2000). The search for informed traders in the totals betting market for National Basketball Association games. Journal of Sports Economics, 1(2), 177–186. https://doi.org/10.1177/152700250000100205
11.
GandarJ. M.ZuberR. A.JohnsonR. S. (2001). Searching for the favourite-longshot bias down under: An examination of the New Zealand pari-mutuel betting market. Applied Economics, 33(13), 1621–1629. https://doi.org/10.1080/00036840010012951
GettyD.LiH.YanoM.GaoC.HosoiA. (2018). Luck and the law: Quantifying chance in fantasy sports and other contests. SIAM Review, 60(4), 869–887. https://doi.org/10.1137/16M1102094
14.
GilovichT.ValloneR.TverskyA. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), 295–314. https://doi.org/10.1016/0010-0285(85)90010-6
15.
GrahamM.McKinneyC. N. (2009). The effect of late money on betting market efficiency. Applied Economics Letters, 16(4), 369–372. https://doi.org/10.1080/13504850601018577
16.
GrammM.McKinneyC. N.ParkerR. E. (2016). Late money and betting market efficiency: Evidence from Australia. The Journal of Gambling Business and Economics, 10(2), 11–21. https://doi.org/10.5750/jgbe.v10i2.1246
17.
GreenB.ZwiebelJ. (2018). The hot-hand fallacy: Cognitive mistakes or equilibrium adjustments? Evidence from major league baseball. Management Science, 64(11), 5315–5348. https://doi.org/10.1287/mnsc.2017.2804
18.
JannB. (2013). COEFPLOT: Stata module to plot regression coefficients and other results [Statistical Software Components (S457686)]. Boston College Department of Economics.
19.
JegadeeshN.TitmanS. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65–91. https://doi.org/10.1111/j.1540-6261.1993.tb04702.x
20.
JegadeeshN.TitmanS. (2001). Profitability of momentum strategies: An evaluation of alternative explanations. The Journal of Finance, 56(2), 699–720.
21.
KahnemanD. (2011). Thinking, fast and slow. Macmillan.
22.
KriegerK.FodorA. (2013). Price movements and the prevalence of informed traders: The case of line movement in college basketball. Journal of Economics and Business, 68, 70–82. https://doi.org/10.1016/j.jeconbus.2013.04.002
23.
LakonishokJ.LeeI. (2001). Are Insider Trades Informative?The Review of Financial Studies, 14(1), 79–111. https://doi.org/10.1093/rfs/14.1.79
24.
LeeE.PiqueiraN. (2019). Behavioral biases of informed traders: Evidence from insider trading on the 52-week high. Journal of Empirical Finance, 52, 56–75. https://doi.org/10.1016/j.jempfin.2019.02.007
LosakJ. M. (2021). Player pricing mechanisms and the daily fantasy sport chance versus skill debate. International Journal of Sport Finance, 16(4), 206–222. https://doi.org/10.32731/IJSF/164.112021.04
27.
LosakJ. M.WeinbachA. P.PaulR. J. (2023). Behavioral biases in daily fantasy baseball: The case of the hot hand. Journal of Sports Economics, 24(3), 374–401. https://doi.org/10.1177/15270025221128955
28.
MeehanJ. C. (2015). The predominate goliath: Why pay-to-play daily fantasy sports are games of skill under the dominant factor test. Marquette Sports Law Review, 26(1), 5–36.
MillerJ. B.SanjurjoA. (2018). Surprised by the hot hand fallacy? A truth in the law of small numbers. Econometrica, 86(6), 2019–2047. https://doi.org/10.3982/ECTA14943
PapkeL. E.WooldridgeJ. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.
33.
PaulR. J.WeinbachA. P.HumphreysB. (2011). Revisiting the “hot hand” hypothesis in the NBA betting market using actual sportsbook betting percentages on favorites and underdogs. Journal of Gambling Business & Economics, 5(2), 42–56. https://doi.org/10.5750/jgbe.v5i2.569
34.
PaulR. J.WeinbachA. P.HumphreysB. (2013). Sportsbook pricing and informed bettors in the early and late season in the NBA. Academy of Economics and Finance Journal, 4, 69–74.
35.
PaulR. J.WeinbachA. P.HumphreysB. (2014). Bettor Belief in the “hot hand”: Evidence from detailed betting data on the NFL. Journal of Sports Economics, 15(6), 636–649. https://doi.org/10.1177/1527002512465414
36.
PaulR. J.WeinbachA.LosakJ. (2020). Market efficiency and the setting of salaries for NBA daily fantasy on FanDuel and DraftKings. Journal of Prediction Markets, 14(2), 45–60. https://doi.org/10.5750/jpm.v14i2.1813
37.
SauerR. D. (2005). The state of research on markets for sports betting and suggested future directions. Journal of Economics and Finance, 29, 416–426. https://doi.org/10.1007/BF02761586
38.
SauerR. D. (1998). The economics of wagering markets. Journal of Economic Literature, 36(4), 2021–2064.
39.
StataCorp. (2019). Stata Statistical Software: Release 16. StataCorp LLC.
SuhonenN.SaastamoinenJ.KainulainenT.ForrestD. (2018). Is timing everything in horse betting? Bet amount, timing and bettors' returns in pari-mutuel wagering markets. Economics Letters, 173, 97–99. https://doi.org/10.1016/j.econlet.2018.09.021
42.
SurowieckiJ. (2005). The wisdom of crowds. Anchor.
ZiobrowskiA. J.ChengP.BoydJ. W.ZiobrowskiB. J. (2004). Abnormal returns from the common stock investments of the U.S. Senate. The Journal of Financial and Quantitative Analysis, 39(4), 661–676.
