Abstract
Fixed odds betting is a popular mechanism in sports game betting. In this paper, we aim to decipher actual group belief on contingent future events from the dynamics of fixed odds betting. Different from previous studies, we adopt the prospect theory rather than the expected utility (EU) theory to model bettor behaviors. Thus, we do not need to make assumptions on how much each bettor stake on their preferred events. We develop a model that captures the heterogeneity of bettors with behavior parameters drawn from beta distributions. We evaluate our proposed model on a real-world dataset collected from online betting games for 2008 Olympic Game events. In the empirical study, our model significantly outperforms expert (bookmaker) predictions. Our study shows the possibility of developing a light-weight derivative prediction market upon fixed odds betting for collective information analysis and decision making.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 54-63 |
| Number of pages | 10 |
| Volume | 6749 LNCS |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
| Event | Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2011 - Beijing, China Duration: Jul 9 2011 → Jul 9 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6749 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2011 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 7/9/11 → 7/9/11 |
Fingerprint
Keywords
- computational experiments
- fixed odds betting
- prediction markets
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Estimating collective belief in fixed odds betting. / Chen, Weiyun; Li, Xin; Zeng, Dajun.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6749 LNCS 2011. p. 54-63 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6749 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimating collective belief in fixed odds betting
AU - Chen, Weiyun
AU - Li, Xin
AU - Zeng, Dajun
PY - 2011
Y1 - 2011
N2 - Fixed odds betting is a popular mechanism in sports game betting. In this paper, we aim to decipher actual group belief on contingent future events from the dynamics of fixed odds betting. Different from previous studies, we adopt the prospect theory rather than the expected utility (EU) theory to model bettor behaviors. Thus, we do not need to make assumptions on how much each bettor stake on their preferred events. We develop a model that captures the heterogeneity of bettors with behavior parameters drawn from beta distributions. We evaluate our proposed model on a real-world dataset collected from online betting games for 2008 Olympic Game events. In the empirical study, our model significantly outperforms expert (bookmaker) predictions. Our study shows the possibility of developing a light-weight derivative prediction market upon fixed odds betting for collective information analysis and decision making.
AB - Fixed odds betting is a popular mechanism in sports game betting. In this paper, we aim to decipher actual group belief on contingent future events from the dynamics of fixed odds betting. Different from previous studies, we adopt the prospect theory rather than the expected utility (EU) theory to model bettor behaviors. Thus, we do not need to make assumptions on how much each bettor stake on their preferred events. We develop a model that captures the heterogeneity of bettors with behavior parameters drawn from beta distributions. We evaluate our proposed model on a real-world dataset collected from online betting games for 2008 Olympic Game events. In the empirical study, our model significantly outperforms expert (bookmaker) predictions. Our study shows the possibility of developing a light-weight derivative prediction market upon fixed odds betting for collective information analysis and decision making.
KW - computational experiments
KW - fixed odds betting
KW - prediction markets
UR - http://www.scopus.com/inward/record.url?scp=79960261413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79960261413&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22039-5_6
DO - 10.1007/978-3-642-22039-5_6
M3 - Conference contribution
AN - SCOPUS:79960261413
SN - 9783642220388
VL - 6749 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 63
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -