With the advent of formats of limited overs in cricket like the One Day and Twenty-20 Internationals, test cricket, the longest-running version of the game, has witnessed a significant decline in popularity. The World Test Championship (WTC) was launched in 2019 by the International Cricket Council to reignite interest in test cricket. However, the initial point distribution system devised for the inaugural WTC had several shortcomings, which include allocating points based on the outcome of the entire series rather than the number of test matches played, the absence of rewards for teams winning matches away from home, failing to take into consideration the margin of victory (MOV) and not accounting for the relative strength of competing teams. Considering these shortcomings, this article proposes a new point system, thus providing an alternative to the existing one. The point system uses the Elo rating system, including factors such as home-field advantage, impact of the toss and MOV. Statistical analyses were conducted to validate the home-field advantage and toss impact. Subsequently, the proposed model developed was named the points won per match system. The team rankings in the WTC are established based on this proposed model and compared with the actual one.
AllsoppP. (2005). Measuring team performance and modelling the home advantage effect in cricket [Doctoral dissertation]. Swinburne University of Technology.
2.
AllsoppP., & ClarkeS. R. (2001). Methods for quantifying performances in one-day cricket. In CohenG. & LangtryT., (Eds.), Fifth Conference on mathematics and computers in sport (pp. 28–36). University of Technology.
3.
AllsoppP. E., & ClarkeS. R. (2004). Rating teams and analysing outcomes in one-day and test cricket. Journal of the Royal Statistical Society Series A: Statistics in Society, 167(4), 657–667.
4.
AngeliniG., CandilaV., & De AngelisL. (2022). Weighted Elo rating for tennis match predictions. European Journal of Operational Research, 297(1), 120–132.
5.
BarnettT., & PollardG. H. (2011). Devising new Australian rules football scoring systems. Journal of Quantitative Analysis in Sports, 7(3). 10.2202/1559-0410.1338
6.
BarrowD., DrayerI., ElliottP., GautG., & OstingB. (2013). Ranking rankings: an empirical comparison of the predictive power of sports ranking methods. Journal of Quantitative Analysis in Sports, 9(2), 187–202.
7.
BergA. (2020). Statistical analysis of the Elo rating system in chess. Chance, 33(3), 31–38.
8.
CarboneJ., CorkeT., MoisiadisF. (2016). The rugby league prediction model: Using an Elo-based approach to predict the outcome of National Rugby League (NRL) matches. International Educational Scientific Research Journal, 2(5), 26–30.
9.
ConstantinouA. C., & FentonN. E. (2013). Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. Journal of Quantitative Analysis in Sports, 30(9), 37–50.
10.
DawsonP., MorleyB., PatonD., & ThomasD. (2009). To bat or not to bat: An examination of match outcomes in day-night limited overs cricket. Journal of the Operational Research Society, 60(12), 1786–1793.
11.
De SilvaB. M., PondG. R., & SwartzT. B. (2001). Applications: Estimation of the magnitude of victory in one‐day cricket RMIT University, Mayo Clinic Rochester and Simon Fraser University. Australian & New Zealand Journal of Statistics, 43(3), 259–68.
12.
De SilvaB. M., & SwartzT. B. (1998). Winning the coin toss and the home team advantage in one-day international cricket matches. Department of Statistics and Operations Research, Royal Melbourne Institute of Technology.
13.
DewenterR. (2003). Raising the scores?: Empirical evidence on the introduction of the three-point rule in Portuguese football [Working Paper No. 22/2003]. Helmut Schmidt University.
14.
ForrestD., & DorseyR. (2008). Effect of toss and weather on County Cricket Championship outcomes. Journal of Sports Sciences, 26(1), 3–13.
15.
GomezM. A., & PollardR. (2011). Reduced home advantage for basketball teams from capital cities in Europe. European Journal of Sport Science, 11(2), 143–148.
16.
HarvilleD. (1977). The use of linear-model methodology to rate high school or college football teams. Journal of the American Statistical Association, 72(358), 278–89.
17.
HoffmannM. D., LougheadT. M., DixonJ. C., & CrozierA. J. (2017). Examining the home advantage in the National Hockey League: Comparisons among regulation, overtime, and the shootout. Psychology of Sport and Exercise, 28(1), 24–30.
18.
KovalchikS. (2020). Extension of the Elo rating system to margin of victory. International Journal of Forecasting, 36(4), 1329–1341.
19.
MarekP., & VávraF. (2020). Home team advantage in the English Premier League. Metodoloski Zvezki, 17(1), 55–76.
20.
MorleyB., & ThomasD. (2005). An investigation of home advantage and other factors affecting outcomes in English one-day cricket matches. Journal of Sports Sciences, 23(3), 261–268.
21.
OrchardJ. W., JamesT., & PortusM. R. (2006). Injuries to elite male cricketers in Australia over a 10-year period. Journal of Science and Medicine in Sport, 9(6), 459–467.
22.
PojskicH., SeparovicV., & UzicaninE. (2011). Modelling home advantage in basketball at different levels of competition. Acta Kinesiologica, 5(1), 25–30.
23.
PollardR. (1986). Home advantage in soccer: A retrospective analysis. Journal of Sports Sciences, 4(3), 237–248.
24.
PriyadarshiA. (2015). The Indian Premier League changing the rules of the game. International Journal of Engineering Researches and Management Studies, 2(8), 11–15.
25.
RayS., & RoychowdhuryS. (2021). Cricket mix optimization using heuristic framework after ensuring Markovian equilibrium. Journal of Sports Analytics, 7(3), 155–168.
26.
SaikiaH., & BhattacharjeeD. (2010). On the effect of home team advantage and winning the toss in the outcome in T20 International cricket matches. Assam University Journal of Science and Technology, 16(6/2), 88–93.
27.
SeckinA., & PollardR. (2008). Home advantage in Turkish professional soccer. Perceptual and Motor Skills, 107(1), 51–54.
28.
StefaniR., & PollardR. (2007). Football rating systems for top-level competition: A critical survey. Journal of Quantitative Analysis in Sports, 3(3). 10.2202/1559-0410.1071
29.
SullivanC., & CroninC. (2016). Improving Elo rankings for sports experimenting on the English premier league. [Virginia Tech CSx824/ECEx424 Technical Report]. https://courses.cs.vt.edu/cs5824/Fall15/project_reports/sullivan_cronin.pdf
30.
TasharrofiS., & BarnesJ. C. (2022). Optimizing community supervision practices with the Elo-rating system: A proof-of-concept. Justice Evaluation Journal, 5(2), 288–306.
31.
TrombleyM. J. (2016). Does artificial grass affect the competitive balance in Major League Soccer?Journal of Sports Analytics, 1(2), 73–87.
32.
VazL., CarrerasD., & KraakW. (2012). Analysis of the effect of alternating home and away field advantage during the Six Nations Rugby Championship. International Journal of Performance Analysis in Sport, 12(3), 593–607.
33.
WatsonN., HendricksS., StewartT., & DurbachI. (2021). Integrating machine learning and decision support in tactical decision-making in rugby union. Journal of the Operational Research Society, 72(10), 2274–2285.
34.
WolfS., SchmittM., & SchullerB. (2020). A football player rating system. Journal of Sports Analytics, 6(4), 243–257. 10.3233/JSA-200411
