Analysis of team success based on technical match-play performance in the Australian Football League Women’s (AFLW) competition

Figures & data

Table 1. Description of data detail available and use in previous women’s Australian football literature.

Data	Rationale	Used in AFLW literature	Examples
Basic Statistics	Basic statistics – Simplest baseline	Yes (2017-18 seasons only) Black et al. (2019); Cust et al. (2019)	Kicks, Handballs, Marks
Advanced Statistics – Metres Gained Statistics	Additional variables representing information of team performance in terms of the total distance gained on the field by a team in a match.	No.	Metres Gained, Metres Gained Retained, Chain Metres
Relative between Teams	Relative performance in a variable to the other team that is often key to understanding match-play success.	Yes (2017-18 seasons only) Cust et al. (2019).	Kicks_Relative (Team Total Kicks – Opposition Total Kicks)
Locational Component	Includes where statistics occur which can give information of why these statistics are important. Five zones on the field as determined and assessed by Champion Data (Attacking Midfield, Defensive Midfield, True Midfield, Forward 50 & Defensive 50). Forward 50 results were also removed in certain dataset iterations as it was anticipated that having the location where almost all scoring actions occur in the data may bias prediction.	No.	Kicks_AM – Attacking Midfield Kicks. Kicks_F50 – Forward 50 Kicks Kicks_D50 – Defensive 50 Kicks. Kicks_DM – Defensive Midfield Kicks.
Feature Distributions of Player Contributions	Represent the contributions of player thresholds (e.g. the sum of how many goals the top 5/20/50% of players [highest goal player, top 4, top 11 players] scored in a game/quarter) and use that as a predictor. Means comment on player contributions to team can be made as in Cust et al. (2019).	Yes (2017-18 seasons only). Only done on 12 variable datasets Cust et al. (2019).	Kicks_5per – Kicks attributable to the top 5% of players (approx. 1 player) on the team by kicks. Kicks_25per - Kicks attributable to the top 25% of players (approx. 5 players) on the team by kicks. Kicks_50per - Kicks attributable to the top 50% of players (approx. 10 players) on the team by kicks.

Table 2. Combinations of data used to fit models to predict Australian Football League Women’s (AFLW) match outcomes.

Model set + iteration	Dataset combination used	Number of variables used
Model 1.1	Baseline Data	27
Model 1.2	Model 1.1 + Relative between Teams	54
Model 2.1	All Statistics Data	320
Model 2.2	Model 2.1 + Relative between Teams	640
Model 3.1	Zone of Field Data with Baseline variables	130
Model 3.2	Model 3.1 + Relative between Teams	260
Model 3.3	Model 3.2 - Forward 50 Statistics removed	208
Model 4.1	Model 3.1 + Metres Gained variables	390
Model 4.2	Model 4.1 + Relative between Teams	780
Model 4.3	Model 4.2 - Forward 50 Statistics removed	624
Model 5.1	Model 3.1 + Feature Distributions of Players on Team	520
Model 5.2	Model 5.1 + Relative between Teams	1040
Model 5.3	Model 5.2 - Forward 50 Statistics removed	832

Figure 1. Model performance to determine which technical match-play data best predicts Australian Football League Women’s competition match outcomes in terms of difference in mean absolute error and prediction accuracy relative to the baseline Model 1.1.

Table 3. Twenty most important variables for each model fit to predict quarter margins in the Australian Football League Women’s competition and accompanying variable importance score.

Highest importance variables for each model
Model 1.1	MDGI	Model 1.2	MDGI	Model 2.1	MDGI	Model 2.2	MDGI
INSIDE_50	49272.47	INSIDE_50_relative	81934.22	CHAIN_METRES_NET	135352.69	CHAIN_METRES_relative	144338.62
EFFECTIVE_KICK	19686.92	EFFECTIVE_KICK_relative	33416.41	CHAIN_METRES_NET_PG	10473.92	TOTAL_GAINED_METRES_relative	48234.55
KICK	16271.30	KICK_relative	22169.26	IN50_RPT_RETURN	4698.35	KICK_GAIN_METRES_relative	8965.94
TURNOVER	11302.89	REBOUND_50_relative	17609.66	IN50_MID_STOP_RETURN	3488.84	CHAIN_METRES_NET_relative	2778.08
CLEARANCE	9111.40	DISPOSAL_EFFICIENCY_relative	4102.15	CLEARANCE_WIN_PCT	3345.25	METRES_GAINED_RETAINED_relative	2241.13
REBOUND_50	9104.72	MARK_relative	3984.27	KICK_GAIN_METRES	3212.32	IN50_RPT_RETURN_relative	1553.17
KICK_EFFICIENCY	8968.47	TURNOVER_relative	3764.70	METRES_GAINED_RETAINED	2952.41	METRES_GAINED_NET_relative	1100.19
EFFECTIVE_DISPOSAL	8643.47	KICK_EFFICIENCY_relative	3319.92	KICK	2190.50	IN50_TRANS_RETURN_relative	1050.90
TACKLE	8071.75	CLEARANCE_relative	2943.41	METRES_GAINED_NET	1541.23	IN50_MID_STOP_RETURN_relative	771.69
DISPOSAL	7748.23	INTERCEPT_relative	2738.92	METRES_GAINED_EFF	1497.84	REBOUND_50_relative	355.28
DISPOSAL_EFFICIENCY	7337.57	EFFECTIVE_DISPOSAL_relative	2668.35	TOTAL_GAINED_METRES	1280.18	IN50_INIT_RETURN_relative	344.07
KICK_TO_HANDBALL_RATIO	6827.40	TACKLE_relative	2654.14	SQUAD_PRESSURE_CHANCE	1208.62	IN50_KICK_RETAIN_PCT_relative	282.33
HANDBALL_EFFICIENCY	6479.93	INSIDE_50	2563.05	IN50_INIT_RETURN	1125.00	METRES_GAINED_RETAINED	280.06
FREE_FOR	6162.29	HANDBALL_EFFICIENCY_relative	2387.28	IN50_TRANS_RETURN	1046.00	METRES_GAINED_EFF_relative	273.15
HARD_BALL_GET	5985.83	REBOUND_50	2368.96	SQUAD_TACKLE_EFFICIENCY	945.76	SHRT_KICK_FROM_POS_EFF_PCT_relative	272.78
BEHIND	5757.23	HANDBALL_EFFICIENCY	2088.20	CHAIN_METRES	906.50	IN50_RPT_RETURN	233.11
MARK	5685.83	KICK_EFFICIENCY	1958.74	CHAIN_LAUNCH	877.91	IN50_RPT_DIR_RETURN_relative	223.67
INTERCEPT	5672.25	DISPOSAL_EFFICIENCY	1950.00	CHAIN_METRES_NET_STOP	868.58	CHAIN_METRES_NET	217.97
FREE_AGAINST	5095.78	CLEARANCE	1877.97	PLY_TACKLE_EFFICIENCY	789.82	IN50_LAUNCH_PCT_relative	211.61
HITOUT	4815.72	KICK_TO_HANDBALL_RATIO_relative	1814.06	SQUAD_PRESSURE_FACTOR	779.17	DISPOSAL_EFFICIENCY_relative	209.78
Model 3.1		MDGI	Model 3.2		MDGI	Model 3.3	MDGI
EFFECTIVE_KICK_F50		97738.94	EFFECTIVE_KICK_F50_relative		178054.78	INSIDE_50_AM_relative	96523.12
INSIDE_50_AM		9502.39	KICK_EFFICIENCY_F50_relative		1183.11	REBOUND_50_D50_relative	34659.13
KICK_EFFICIENCY_F50		7726.63	DISPOSAL_EFFICIENCY_AM_relative		1151.37	INSIDE_50_DM_relative	7854.96
KICK_F50		6680.79	KICK_EFFICIENCY_AM_relative		1035.39	EFFECTIVE_KICK_AM_relative	4902.23
FREE_AGAINST_D50		4704.60	KICK_TO_HANDBALL_RATIO_AM_relative		1014.21	DISPOSAL_EFFICIENCY_AM_relative	3800.87
DISPOSAL_F50		2880.59	INSIDE_50_AM_relative		942.60	FREE_AGAINST_D50_relative	2821.65
EFFECTIVE_DISPOSAL_F50		2862.27	DISPOSAL_EFFICIENCY_F50_relative		872.95	KICK_EFFICIENCY_AM_relative	2558.10
KICK_TO_HANDBALL_RATIO_DM		2724.42	KICK_EFFICIENCY_AM		765.46	EFFECTIVE_DISPOSAL_AM_relative	2517.28
TACKLE_AM		2501.62	DISPOSAL_EFFICIENCY_AM		755.58	REBOUND_50_D50	2451.83
DISPOSAL_EFFICIENCY_D50		2342.14	CLEARANCE_DM		754.60	INSIDE_50_AM	2259.00
TURNOVER_D50		2313.29	KICK_TO_HANDBALL_RATIO_DM		722.07	DISPOSAL_EFFICIENCY_D50_relative	1882.37
KICK_EFFICIENCY_DM		2296.03	KICK_TO_HANDBALL_RATIO_F50_relative		686.52	INTERCEPT_D50_relative	1523.72
KICK_DM		2269.08	EFFECTIVE_KICK_F50		685.46	CLEARANCE_DM	1494.74
KICK_TO_HANDBALL_RATIO_D50		2236.74	HITOUT_DM		668.36	DISPOSAL_EFFICIENCY_DM_relative	1421.82
DISPOSAL_EFFICIENCY_AM		2220.45	HANDBALL_EFFICIENCY_AM_relative		622.75	INSIDE_50_DM	1333.55
KICK_TO_HANDBALL_RATIO_AM		2203.54	HANDBALL_EFFICIENCY_DM_relative		612.28	HANDBALL_EFFICIENCY_DM_relative	1208.78
FREE_AGAINST_DM		2095.21	KICK_TO_HANDBALL_RATIO_F50		608.31	DISPOSAL_EFFICIENCY_AM	1194.86
DISPOSAL_EFFICIENCY_DM		2005.34	TACKLE_AM_relative		608.13	CLEARANCE_DM_relative	1184.63
KICK_TO_HANDBALL_RATIO_F50		1989.77	KICK_TO_HANDBALL_RATIO_D50_relative		606.38	HANDBALL_EFFICIENCY_D50_relative	1172.27
HANDBALL_EFFICIENCY_DM		1973.35	KICK_TO_HANDBALL_RATIO_DM_relative		595.96	KICK_EFFICIENCY_DM_relative	1123.79

MDGI = Mean Decrease in Gini Index. AM = Attacking Midfield, DM = Defensive Midfield, F50 = Forward 50, D50 = Defensive 50, PG = Possession Gain,PCT = Percent, RPT = Repeat, STOP = Stoppage, EFF = Effective, TRANS = Transition, PLY = Player, DIR = Direct. Further definitions available in supplementary material. Variables in bold are those with a negative correlation to the dependent variables (quarter margin).

Table 4. Twenty most important variables for each model fit to predict quarter margins in the Australian Football League Women’s competition and accompanying variable importance score.

Model 4.1	MDGI	Model 4.2	MDGI	Model 4.3	MDGI
EFFECTIVE_KICK_F50	62270.47	EFFECTIVE_KICK_F50_relative	80392.66	CHAIN_METRES_NET_AM_relative	45049.71
METRES_GAINED_RETAINED_F50	38798.35	METRES_GAINED_RETAINED_F50_relative	45228.34	METRES_GAINED_RETAINED_AM_relative	21404.54
CHAIN_METRES_NET_D50	6282.06	METRES_GAINED_EFF_F50_relative	27420.53	TOTAL_GAINED_METRES_AM_relative	19010.99
CHAIN_METRES_NET_DM	6124.34	KICK_GAIN_METRES_F50_relative	8837.95	CHAIN_METRES_NET_D50_relative	16737.91
TOTAL_GAINED_METRES_AM	6050.35	EFFECTIVE_DISPOSAL_F50_relative	6555.74	CHAIN_METRES_NET_DM_relative	11128.90
KICK_EFFICIENCY_F50	4885.64	TOTAL_GAINED_METRES_F50_relative	5541.99	KICK_GAIN_METRES_AM_relative	8006.19
KICK_GAIN_METRES_F50	4000.54	KICK_EFFICIENCY_F50_relative	3442.13	METRES_GAINED_NET_DM_relative	5916.67
METRES_GAINED_EFF_F50	3935.75	METRES_GAINED_NET_F50_relative	3357.98	METRES_GAINED_EFF_AM_relative	5422.25
KICK_GAIN_METRES_AM	3778.67	METRES_GAINED_RETAINED_F50	2383.32	TOTAL_GAINED_METRES_DM_relative	4274.89
CHAIN_METRES_NET_AM	3777.39	METRES_GAINED_RETAINED_AM_relative	2061.43	CHAIN_METRES_NET_PG_D50_relative	3053.62
TOTAL_GAINED_METRES_F50	3552.69	EFFECTIVE_KICK_F50	2032.86	INSIDE_50_AM_relative	2929.15
CHAIN_METRES_NET_PG_DM	3414.19	KICK_F50_relative	1349.01	CHAIN_METRES_NET_D50	2419.94
CHAIN_METRES_AM	3199.57	METRES_GAINED_EFF_F50	1330.08	CHAIN_METRES_NET_DM	2325.74
CHAIN_METRES_NET_PG_D50	3145.54	CHAIN_METRES_NET_D50_relative	1294.34	KICK_GAIN_METRES_DM_relative	2096.88
METRES_GAINED_NET_AM	3053.11	CHAIN_METRES_NET_AM_relative	1212.14	CHAIN_METRES_NET_M_relative	2091.75
KICK_F50	3040.17	METRES_GAINED_EFF_AM_relative	873.87	METRES_GAINED_NET_AM_relative	2025.58
METRES_GAINED_RETAINED_AM	1989.78	DISPOSAL_EFFICIENCY_F50_relative	868.35	CHAIN_METRES_NET_CB_M_relative	1830.08
EFFECTIVE_DISPOSAL_F50	1708.01	TOTAL_GAINED_METRES_DM_relative	630.18	CHAIN_METRES_NET_ST_M_relative	1664.43
CHAIN_METRES_NET_PG_AM	1627.93	CHAIN_METRES_NET_DM_relative	629.92	CHAIN_METRES_NET_PG_DM_relative	1407.21
METRES_GAINED_EFF_AM	1316.09	CHAIN_METRES_NET_PG_AM_relative	561.19	METRES_GAINED_RETAINED_AM	1194.83
Model 5.1	MDGI	Model 5.2	MDGI	Model 5.3	MDGI
EFFECTIVE_KICK_F50_50per	22347.56	EFFECTIVE_KICK_F50_25per_relative	48233.24	INSIDE_50_AM_relative	51769.97
EFFECTIVE_KICK_F50_25per	21987.79	EFFECTIVE_KICK_F50_relative	38396.56	INSIDE_50_AM_50per_relative	41228.64
EFFECTIVE_KICK_F50	20587.25	EFFECTIVE_KICK_F50_50per_relative	37280.68	REBOUND_50_D50_relative	19365.48
KICK_EFFICIENCY_F50_50per	8200.96	KICK_EFFICIENCY_F50_25per_relative	14945.41	REBOUND_50_D50_50per_relative	11590.94
KICK_EFFICIENCY_F50_25per	7732.34	KICK_EFFICIENCY_F50_50per_relative	14361.53	DISPOSAL_EFFICIENCY_AM_50per_relative	3392.33
KICK_TO_HANDBALL_RATIO_F50_50per	5326.61	EFFECTIVE_DISPOSAL_F50_25per_relative	5064.39	FREE_AGAINST_D50_relative	2207.45
KICK_F50_50per	4609.21	EFFECTIVE_DISPOSAL_F50_50per_relative	4138.14	DISPOSAL_EFFICIENCY_AM_relative	2198.40
KICK_F50	4208.14	EFFECTIVE_DISPOSAL_F50_relative	3543.89	INSIDE_50_AM_25per_relative	1930.43
KICK_TO_HANDBALL_RATIO_F50_25per	3754.12	EFFECTIVE_KICK_F50_5per_relative	1896.44	REBOUND_50_D50_25per_relative	1765.27
EFFECTIVE_DISPOSAL_F50	3287.54	DISPOSAL_EFFICIENCY_F50_50per_relative	1324.40	KICK_EFFICIENCY_AM_relative	1685.96
INSIDE_50_AM	3050.04	KICK_TO_HANDBALL_RATIO_F50_50per_relative	1318.24	INSIDE_50_DM_5per_relative	1640.59
KICK_EFFICIENCY_F50	2793.19	DISPOSAL_EFFICIENCY_F50_25per_relative	1218.09	INSIDE_50_DM_25per_relative	1611.20
INSIDE_50_AM_50per	2767.50	KICK_F50_25per_relative	1085.90	INSIDE_50_DM_50per_relative	1577.59
KICK_F50_25per	2452.15	KICK_F50_50per_relative	1063.39	KICK_EFFICIENCY_AM_50per_relative	1567.61
EFFECTIVE_DISPOSAL_F50_25per	2296.20	EFFECTIVE_KICK_F50_50per	1046.36	INSIDE_50_DM_relative	1463.54
EFFECTIVE_DISPOSAL_F50_50per	2037.69	KICK_EFFICIENCY_F50_relative	1043.27	REBOUND_50_D50	1341.09
EFFECTIVE_KICK_F50_5per	2017.55	KICK_TO_HANDBALL_RATIO_F50_25per_relative	988.90	DISPOSAL_EFFICIENCY_D50_relative	1289.05
FREE_AGAINST_D50	1994.44	EFFECTIVE_KICK_F50_25per	902.67	EFFECTIVE_KICK_AM_50per_relative	1288.12
KICK_EFFICIENCY_F50_5per	1746.38	EFFECTIVE_KICK_F50	833.19	EFFECTIVE_KICK_AM_relative	1219.87
INSIDE_50_AM_25per	1155.58	KICK_F50_relative	718.81	EFFECTIVE_KICK_AM_25per_relative	1166.78

MDGI = Mean Decrease in Gini Index. AM = Attacking Midfield, DM = Defensive Midfield, F50 = Forward 50, D50 = Defensive 50, M = True Midfield, EFF = Effective, PG = Possession Gain, 5per = Top 5% of players on team cumulative performance in variable, 25per = Top 25% of players on team cumulative performance in variable, 50per = Top 50% of players on team cumulative performance in variable. Further definitions available in supplementary material. Variables in bold are those with a negative correlation to the dependent variables (quarter margin).

Table 5. Summary table of key practical insights surrounding usage of data details and key variables for coaches and sport practitioners.

Data detail	Conclusions surrounding usage	Key variables
Basic Statistics	Baseline variables commonly publicly available still produce a fair representation although utilisation of relative to other team statistics should always be introduced.	Inside 50s, kicks, effective Kicks, and clearances positive relationship. Turnovers and rebound 50s, negative relationship. All have more predictive power when expressed relative to opposition.
All Statistics	Produced best prediction by mean absolute error. Difficult to acquire all available data given usual restricted access.	Repeat Inside 50s, percentage of clearances won, and Metres Gained statistics covered below.
Advanced Statistics – Metres Gained Statistics	Found to account for a lot of the predictive value in the full dataset especially when interacting with field location. These variables represent the territory on the field gained by a team and should certainly be included or collected for future datasets.	Chain + Total Metres gained; Metres gained retained (i.e. ball remained in possession of the team from disposal). Relative form and in Forward 50 zone add more predictive power.
Relative between Teams	Found to make considerable improvement to prediction. Easy to implement and should be a level of data used for all analysis of team performance.	Covered through other data detail descriptions and seems to produce better prediction.
Locational Component	Should be sought wherever possible and potentially in more detail. Improved prediction substantially. Shows importance of context of where on the field an action occurs which affects variable importance. Caveat that Forward 50 zone seems to dominate prediction, which may be considered data snooping due to the requirement to get inside this zone to score.	Forward 50 locations highly positively associated with team success. Defensive 50 locations highly negatively associated with team success.
Feature Distributions of Player Contributions	Did not improve prediction when introduced. Can be difficult to create so may not be worth effort of producing. Individual player contributions likely still important but may need methodology that can represent whole dataset or alternate means like network analysis to be able to fully capture.	Top 25/50% player contributions to inside 50s.

Black, G. M., Gabbett, T. J., Johnston, R. D., Cole, M. H., Naughton, G., & Dawson, B. (2019). A skill profile of the national women’s Australian football league (AFLW) [article]. Science and Medicine in Football, 3(2), 138–142. https://doi.org/10.1080/24733938.2018.1489140

 Google Scholar

Cust, E. E., Sweeting, A. J., Ball, K., Anderson, H., & Robertson, S. (2019). The relationship of team and individual athlete performances on match quarter outcome in elite women’s Australian rules football [article]. Journal of Science & Medicine in Sport, 22(10), 1157–1162. https://doi.org/10.1016/j.jsams.2019.05.004

 PubMed Web of Science ®Google Scholar

Data availability statement

Data not available due to commercial restrictions. Due to the nature of the research, commercial supporting data is not available.