Like basketball, and baseball cricket is a game where numbers play a big role. Every analyst uses statistics and metrics to measure players’ performances, but it is not limited to batting and bowling, metrics are used for fielding, player selection, and overall game performances. Some metrics like batting average, bowling economy, net run rate, etc. help coaches and analysts understand a team’s strategy, a player’s form, and game prediction. In this article, we will discuss how all the key metrics are calculated with examples.
Table of Contents
Batting Metrics
A. Batting Average
It is one of the oldest metrics in cricket. It is used to determine a player’s ability. The Batting average is calculated by dividing the total number of runs scored by the number of times the player has been dismissed.
Formula: Batting Average = Runs Scored/Number of Times Out
For example, Virat Kohli in his T20I career played 125 matches, made 4188 runs, and was dismissed 86 times, so his batting average would be 4188/86 = 48.69.
The player whose batting average is high shows that he is a very good and consistent batsman. In test cricket, a batsman with an average above 50 is considered an excellent batsman, in limited-overs cricket above 40 is a very good batsman.
B. Strike Rate
The strike rate is an essential metric for both batsmen and bowlers. For bowlers, a lower strike rate is preferable as it indicates the number of balls bowled per wicket taken. It is calculated by dividing the total number of balls bowled by the number of wickets taken.
Formula:
Strike Rate = Number of Balls Bowled / Number of Wickets Taken
For example, Fazalhaq Farooqi bowled 25.2 overs (152 balls) and took 17 wickets in the T20 World Cup 2024. His strike rate would be calculated as 152 ÷ 17 = 8.94.
C. Dot Ball Percentage
Dot ball percentage helps assess the pressure a bowler puts on a batsman by preventing runs. It is calculated by dividing the number of dot balls bowled by the total number of balls bowled and then multiplying by 100.
Formula:
Dot Ball Percentage = (Number of Dot Balls Bowled / Total Balls Bowled) × 100
For example, if a bowler delivers 86 balls and 54 of them are dot balls, the dot ball percentage would be (54 ÷ 86) × 100 = 62.79%.
D. Boundary Percentage
Boundary percentage measures the proportion of a batsman’s total runs that are scored from boundaries (fours and sixes). This metric helps analyze a batsman’s scoring pattern, indicating whether they rely primarily on boundaries or a mix of singles, doubles, and boundaries.
It is calculated by dividing the total number of runs scored from boundaries by the total runs scored and then multiplying by 100.
Formula:
Boundary Percentage = (Total Runs Scored by Boundaries / Total Runs Scored) × 100
For instance, Jake Fraser McGurk scored 330 runs in IPL 2024, hitting 32 fours and 28 sixes. His boundary percentage would be calculated as:
((32 × 4) + (28 × 6)) ÷ 330 × 100 = 89.69%.
Bowling Metrics
A. Bowling Average
The bowling average is one of the most fundamental metrics in cricket, used to assess a bowler’s effectiveness and consistency. It is calculated by dividing the total number of runs conceded by the number of wickets taken.
Formula:
Bowling Average = Total Runs Conceded / Number of Wickets Taken
For example, if a bowler concedes 500 runs and takes 20 wickets, their bowling average would be calculated as 500 ÷ 20 = 25. A lower bowling average indicates better performance, reflecting a bowler’s ability to take wickets while conceding fewer runs.
B. Strike Rate
The strike rate is a critical metric for both batsmen and bowlers. For bowlers, a lower strike rate is more desirable as it represents the average number of balls bowled per wicket taken. It is calculated by dividing the total number of balls bowled by the number of wickets taken.
Formula:
Strike Rate = Number of Balls Bowled / Number of Wickets Taken
For example, Fazalhaq Farooqi bowled 25.2 overs (152 balls) in the T20 World Cup 2024 and took 17 wickets. His strike rate would be calculated as 152 ÷ 17 = 8.94. A lower strike rate indicates a bowler’s ability to take wickets more frequently.
C. Economy Rate
The economy rate measures a bowler’s efficiency in conceding runs. It is calculated by dividing the number of runs conceded by the number of overs bowled.
Formula:
Economy Rate = Number of Runs Conceded / Number of Overs Bowled
For example, Sunil Narine bowled 55 overs and conceded 368 runs in the IPL 2024. His economy rate would be 368 ÷ 55 = 6.69. A good economy rate is typically below 4 in Test cricket and below 6 in T20 cricket, indicating the bowler’s ability to restrict runs.
D. Dot Ball Percentage
The dot ball percentage is an indicator of how well a bowler can apply pressure on a batsman by preventing them from scoring. It is calculated by dividing the number of dot balls bowled by the total number of balls bowled and then multiplying by 100.
Formula:
Dot Ball Percentage = (Number of Dot Balls Bowled / Total Balls Bowled) × 100
For example, if a bowler delivers 86 balls and 54 of them are dot balls, the dot ball percentage would be (54 ÷ 86) × 100 = 62.79%. A higher dot ball percentage indicates the bowler’s ability to restrict the scoring rate and maintain control over the batsman.
Fielding Metrics in Cricket
While we’ve discussed various batting and bowling metrics, fielding also has key metrics that evaluate a player’s performance on the field. Let’s explore these essential fielding metrics:
A. Catch Efficiency
Catch efficiency measures a fielder’s success rate in taking catches. It is calculated by dividing the number of catches taken by the number of catch opportunities (attempted catches) and multiplying by 100.
Formula:
Catch Efficiency = (Catches Taken / Catches Attempted) × 100
For example, if a player has 5 catch opportunities in a match and successfully takes 4 catches, the catch efficiency would be calculated as:
(4 ÷ 5) × 100 = 80%.
B. Fielding Average
The fielding average assesses a player’s contribution by measuring the total number of runs saved across matches. It is calculated by dividing the total runs saved by the number of matches played.
Formula:
Fielding Average = Total Runs Saved / Number of Matches Played
For instance, if a player saves 120 runs over 15 matches, the fielding average would be:
120 ÷ 15 = 8 runs saved per match.
Team Metrics and Advanced Metrics
1. Net Run Rate (NRR)
Net Run Rate is a statistical method used to rank teams with the same number of points in a limited-overs competition. It is calculated by subtracting the average runs conceded per over from the average runs scored per over by the team.
Formula:
NRR = (Total Runs Scored by the Team / Total Overs Faced by the Team) – (Total Runs Conceded by the Team / Total Overs Bowled by the Team)
For example, if a team scores 900 runs in 90 overs and concedes 800 runs in 90 overs, the Net Run Rate would be:
(900 ÷ 90) – (800 ÷ 90) = +1.11.
2. Win/Loss Ratio
The Win/Loss Ratio determines a team’s success rate by comparing the number of matches won to the number of matches lost.
Formula:
Win/Loss Ratio = Number of Matches Won / Number of Matches Lost
For example, if a team plays 8 matches, wins 5, and loses 3, the Win/Loss Ratio would be:
5 ÷ 3 = 1.67.
3. True Bowler Index (TBI)
The True Bowler Index (TBI) is an advanced metric that provides a more comprehensive assessment of a bowler’s performance. Unlike traditional metrics such as bowling average, economy, or strike rate, TBI takes into account factors like pitch conditions, opposition strength, and match context.
For instance, consider two fast bowlers, Bumrah and Shami, who played a T20 match at Sharjah — a known small ground where runs are typically easy to come by. TBI would adjust their performance metrics based on these contextual factors to provide a more accurate reflection of their effectiveness.
Now we will calculate TBI.
Here are some information:
Bumrah:
| Overs Bowled | Runs Conceded | Wickets Taken | Bowling Strike Rate | Bowling Average | Economy Rate |
| 4 | 30 | 2 | 12 | 15 | 7.5 |
Shami:
| Overs Bowled | Runs Conceded | Wickets Taken | Bowling Strike Rate | Bowling Average | Economy Rate |
| 4 | 34 | 3 | 8 | 11.33 | 8.5 |
Applying Adjustment Factors and Contextual Multipliers
To better evaluate the performance of bowlers in specific match situations, we apply adjustment factors and contextual multipliers. These adjustments help reflect the actual impact of their performance based on the quality of wickets taken and match conditions.
Adjustment Factors:
- Bumrah’s Adjustment Factor:
Bumrah has an adjustment factor of 0.9 because he took wickets of top-order batsmen. A factor less than 1 decreases his bowling average to indicate a higher impact. - Shami’s Adjustment Factor:
Shami has an adjustment factor of 1.1 because he primarily dismissed lower-order batsmen. A factor greater than 1 increases his bowling average to reflect a lesser impact.
Adjusted Bowling Metrics:
- Bumrah’s Adjusted Bowling Average:
15 (original average) × 0.9 = 13.5 - Shami’s Adjusted Bowling Average:
11.33 (original average) × 1.1 = 12.46 - Bumrah’s Adjusted Strike Rate:
12 (original strike rate) × 0.95 = 11.4 - Shami’s Adjusted Strike Rate:
8 (original strike rate) × 1.05 = 8.4
Applying Contextual Multipliers:
To further refine the True Bowler Index (TBI), we apply contextual multipliers based on specific match conditions:
- Bumrah’s Contextual Multiplier: 1.1
- Shami’s Contextual Multiplier: 1
Calculating the True Bowler Index (TBI):
- Bumrah’s TBI Calculation:
TBI = (13.5 / 11.4) × (1 / 7.5) × 1.1 = 0.174 - Shami’s TBI Calculation:
TBI = (12.46 / 8.4) × (1 / 8.5) × 1 = 0.174
Analysis of TBI Results:
Shami: Despite having a slightly higher economy rate, Shami also achieved a TBI of 0.174. This reflects his effective performance, even against lower-order batsmen, confirming his value to the team’s bowling attack.
Bumrah: With a TBI of 0.174, Bumrah demonstrated significant impact by maintaining a good economy rate and dismissing key top-order batsmen, indicating his crucial role in the game.
Conclusion
By analyzing all these metrics we can better understand the performance of a player or a team. In today’s cricket where we make strategies using data, these metrics play an important role. By using these metrics a team can improve their game plan and enhance training schedule for players. Ultimately, these metrics can help a team to achieve excellence and consistently win.
