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on Sports and Economics |
| By: | Aitor Calo-Blanco |
| Abstract: | Assessing an individual's contribution within a team remains a fundamental challenge across many domains, particularly when recognition for collective achievements is limited to only a few members. This issue is especially important in professional road cycling, where personal success depends on both individual talent and group effort. Existing points-based ranking systems tend to disproportionately reward high-scoring team leaders while undervaluing domestiques - riders who sacrifice personal success to support group performance. To better capture a rider's impact on the team, we propose a weighted measure of cycling productivity that factors in race points, a redistribution metric, and an adapted version of the CoScore formula. This formula assesses an individual's productivity relative to their teammates' performance. Using data from the 2023 season, we show that our approach offers a comprehensive evaluation of professional cyclists, addressing key limitations of existing ranking systems. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.11831 |
| By: | Avi Arora; Ritesh Malpani |
| Abstract: | Prediction markets offer a natural testbed for trading agents: contracts have binary payoffs, prices can be interpreted as probabilities, and realized performance depends critically on market microstructure, fees, and settlement risk. We introduce PredictionMarketBench, a SWE-bench-style benchmark for evaluating algorithmic and LLM-based trading agents on prediction markets via deterministic, event-driven replay of historical limit-order-book and trade data. PredictionMarketBench standardizes (i) episode construction from raw exchange streams (orderbooks, trades, lifecycle, settlement), (ii) an execution-realistic simulator with maker/taker semantics and fee modeling, and (iii) a tool-based agent interface that supports both classical strategies and tool-calling LLM agents with reproducible trajectories. We release four Kalshi-based episodes spanning cryptocurrency, weather, and sports. Baseline results show that naive trading agents can underperform due to transaction costs and settlement losses, while fee-aware algorithmic strategies remain competitive in volatile episodes. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.00133 |
| By: | Azhan Ahmad (Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam Author-2-Name: Effie L-C. Law Author-2-Workplace-Name: Durham University, Durham, United Kingdom Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:) |
| Abstract: | "Objective - An educator-oriented Serious Game (SG) authoring tool is designed to enable educators to create SGs without expertise in game development, using educator-friendly features such as a no-code programming approach. Methodology - The effectiveness of such a tool should be measured by its usability and by the effectiveness of the games it produces. We have developed the ARQS tool, which is suitable for educators, based on prior usability studies. Findings - In this paper, we present an experimental study comparing games designed by an educator and quiz applications. Novelty - Findings from the study showed that the games led to greater learning gains, motivation, emotional engagement, and learning experience, suggesting that educators can design effective SGs using our ARQS tool. Type of Paper - Empirical" |
| Keywords: | Serious Games; Educator; Experimental Study. |
| JEL: | C73 D83 |
| Date: | 2026–03–31 |
| URL: | https://d.repec.org/n?u=RePEc:gtr:gatrjs:jber268 |
| By: | Joy Das Bairagya; Jonathan Newton; Sagar Chakraborty |
| Abstract: | We present a collaboration ring model -- a network of players playing the prisoner's dilemma game and collaborating among the nearest neighbours by forming coalitions. The microscopic stochastic updating of the players' strategies are driven by their innate nature of seeking selfish gains and shared intentionality. Cooperation emerges in such a structured population through non-equilibrium phase transitions driven by propensity of the players to collaborate and by the benefit that a cooperator generates. The robust results are qualitatively independent of number of neighbours and collaborators. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.11601 |
| By: | Brückmann, G. PhD (University of Bern); El-Ajou, Walid; Stadelmann-Steffen, Isabelle |
| Abstract: | In this pre-registered experiment, we describe how we use a full-fledged serious game, developed in collaboration with energy modellers, energy system experts, and game designers, as an experimental treatment in a large-scale population survey in Switzerland. While previous research has used serious games mostly for targeted and relatively small groups, we assess whether serious gaming has the potential to serve as an effective information tool for the broader population. More specifically, we test whether serious games can influence individual opinion formation on complex issues, such as the energy transition, arguing that the immersive nature of a serious game may be more effective than conventional information treatments in triggering learning effects and ultimately influencing opinion formation. Based on our results, we show that playing the game did not, on a general level, produce any significant effects on either efficacy or policy support. However, the game led to varying reactions among players and influenced support for expanding specific energy sources in accordance with the game’s implications. In light of these nuanced results, we discuss implications for researches and stakeholders. |
| Date: | 2026–01–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:x5u6b_v1 |
| By: | Felipe A. Csaszar; Aticus Peterson; Daniel Wilde |
| Abstract: | Can artificial intelligence outperform humans at strategic foresight -- the capacity to form accurate judgments about uncertain, high-stakes outcomes before they unfold? We address this question through a fully prospective prediction tournament using live Kickstarter crowdfunding projects. Thirty U.S.-based technology ventures, launched after the training cutoffs of all models studied, were evaluated while fundraising remained in progress and outcomes were unknown. A diverse suite of frontier and open-weight large language models (LLMs) completed 870 pairwise comparisons, producing complete rankings of predicted fundraising success. We benchmarked these forecasts against 346 experienced managers recruited via Prolific and three MBA-trained investors working under monitored conditions. The results are striking: human evaluators achieved rank correlations with actual outcomes between 0.04 and 0.45, while several frontier LLMs exceeded 0.60, with the best (Gemini 2.5 Pro) reaching 0.74 -- correctly ordering nearly four of every five venture pairs. These differences persist across multiple performance metrics and robustness checks. Neither wisdom-of-the-crowd ensembles nor human-AI hybrid teams outperformed the best standalone model. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.01684 |