Dual Incentive Analysis in Multi-Stage Competitions: Shadow and Spillover Effects vs. Interrank Spread: IDLab Seminar
As part of the regular scientific seminar, Mikhail Usanin made a report, presenting the results of his joint research with Evgenia Shenkman on “Dual Incentive Analysis in Multi-Stage Competitions: Shadow and Spillover Effects vs. Interrank Spread”. Interrank Spread”.
The study examined the effects of monetary and intangible incentives on team performance in multi-stage competitions using Counter-Strike: Global Offensive (CS:GO) as an example from the professional esports scene. The analysis covered over 3,600 final-stage matches from major tournaments between 2015 and 2022.
The analysis highlighted two key intangible incentive concepts: the Spillover Effect and the Shadow Effect. The Spillover Effect shows that the more rounds a team has played in the past, the worse its current performance will be. This indicates a fatigue effect, even in a digital environment, which is consistent with similar studies in tennis. The Shadow Effect, in turn, reveals how expectation of future opponents influences current motivation. Expecting a weak opponent in the next round increases performance. However, expecting a strong opponent decreases current effort, as teams tend to conserve energy for future rounds. For example, the presence of a "superstar" like Djokovic in the tournament decreases current effort.
Regarding monetary incentives (interrank spread), the study yielded unexpected results. Contrary to classic "Tournament Theory" (Lazear & Rosen), which states that a larger prize gap between places increases motivation, the study found a significant negative effect of the relative prize gap (normalized by the total fund) on team performance. This effect shows a nonlinear U-shaped dependence, reaching a peak negative effect at a gap of about 58% of the total prize pool. The reasons for this require further investigation. Additionally, a dependence on age was revealed: monetary incentives significantly affect only young teams (with an average player age under 23), while the financial gap is not a significant motivator for experienced teams, which is consistent with theories of motivation and accumulated experience.
The research methodology used the difference between won and lost rounds in a match as the dependent variable, or performance/effort indicator. This allowed for a more accurate assessment of a team's dominance than simply considering a win or loss. To measure the shadow effect, HLTV.org ranking data was used to predict the strength of potential future opponents in the tournament grid. The robustness of the results was tested using alternative model specifications, which confirmed the robustness of the findings.
The presentation was followed by a lively discussion. The audience was particularly interested in the unexpected conclusions about monetary motivation. For example, why does a large gap in prize money reduce the teams' fighting spirit, contrary to expectations? Attendees also shared ideas about how to take the influence of future opponents — the so-called "shadow effect" — into account. Such scientific seminars are valuable because the authors received constructive recommendations to refine the methodology and clarify key approaches. These recommendations will help advance the research to the next stage.