# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
## 3 Results  
The data analysis encompassed a diverse set of variables (PIs), necessitating the prioritisation of their significance in predicting match outcomes (win/loss). This prioritisation aimed to determine the most important variables in designing the model, as well as ensure their relevance and impact. The initial random forest model was trained on 136 PIs using 1000 trees with permutation-based importance scoring. This process identified a wide set of PIs influencing match outcomes, with greater importance indicated by higher increases in out-of-bag error upon permutation (Table A1). Lasso logistic regression was applied to the same feature set, shrinking the coefficient of redundant variables to zero. This allows a reduction in the list to a reduced subset of potential KPIs, although Lasso logistic regression alone cannot identify non-linear interactions (Table A2). The hybrid approach was constructed by retraining random forest on the KPIs retrained through lasso logistic regression. Following the approach outlined above, from the 136 PIs in the data (Table A3), 16 PIs were identified through the prediction model and called KPIs. A hybrid model, consisting of the random forest and lasso logistic regression, was formulated to illustrate the relationship between the KPIs and the outcomes of the match. The model constructed, using the 16 KPIs, has a classification accuracy of 85.9%, as assessed through LOOCV. The KPIs variable importance scores are listed in Table 2. Eight KPIs were related to bowling and fielding, and eight to batting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **KPIs** & **Variable Importance** \\ \hline
1 & Number of wickets taken in the last six overs (15 th–20 th) in the & 0.02613 \\
2 & Number of bowlers taking two or more wickets in the second innings & 0.01957 \\
3 & Run rate in the last six overs (15 th–20 th) in the second innings & 0.01302 \\
4 & Number of wickets taken by seam bowlers in the second innings & 0.01216 \\
5 & Batting strike rate in the last six overs (15 th–20 th) in the second innings & 0.00916 \\
6 & Inning single \% in the second innings & 0.00729 \\
7 & Number of wickets taken by spin bowlers in the second innings & 0.00542 \\
8 & Number of sixes scored in the first innings & 0.00392 \\
9 & Number of overs of seam bowling in last six overs (15 th–20 th) in the & 0.00355 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ranking of the sixteen key performance indicators (KPIs) in order of importance with respect to successful match outcomes. Figure 2: Data analysis pseudo-code. The results indicated in Table 2 revealed the relative importance of each KPI in predicting successful match outcomes. It was evident that the KPIs in the second innings, particularly wicket-taking performance and run rate in the final overs, hold significant importance in determining the outcome of cricket matches, as indicated by their high variable importance scores. At the pinnacle of the model lies the significance of wickets taken in the last six overs (15 th-20 th) of the second innings. The ability of the bowling side to secure wickets during this crucial match phase emerged as the single most influential factor in determining match outcomes. This variable has the highest importance score, indicating that the closing phase of a match, particularly defending totals or restricting runs, plays a critical role in determining the match outcome. The run rate in the last six overs (15 th-20 th) of the second innings emerged as another pivotal determinant of match success. This KPI reflects a more distributed bowling performance, where multiple bowlers contribute to maintaining pressure. Such pressure often forces batsmen to take more risks, which can lead to lower run rates or more frequent dismissals, thereby enhancing the accuracy of match outcome predictions. Furthermore, the batting strike rate in the final six overs of the second innings emerged as an influential KPI.