First Innings Performance Analysis: Runs Added

This article is part of a series on cricket analysis at club, age group performance and school level. To see part one, click here.

Once we have expected runs, we can look at measuring performance in the first inning.

Runs Added (R+) is a fast way to accurately judge how a team or individual is doing in the first innings in limited overs cricket. It works both during games, after individual games and as an average for end of season analysis.

With the groundwork of finding xR done, R+ is a simple calculation:

R+ = Runs minus xR

On the team level this is useful for several reasons:

You can see during the match how far ahead or behind Par, predict final score, and adjust tactics appropriately. If using over-by-over scores, you can review the R+ after the game to find out how the innings fluctuated.

You can compare your bat first performance to other teams in the competition. Here’s the one for WDCU in 2019 (non-rain affected matches)

Additionally, You can see how R+ changes over games. Here is a graph showing how average R+ changed over the course of 10 matches for a single team in the WDCU. You can see how after a poor start, the team kept ahead of R+ to finish ahead of expected.

You can apply R+ to partnerships – if you have over by over R+ - and see how they performed. Here is the graph for a WDCU team:

In this example it’s easy to see the first five wickets outperformed the expected performance, although the opening partnership had six below par performances, rescued by a 46-run, +22 opening stand. Additionally, the 4th wicket was most volatile, with three 18+ scores and five below Par scores in 10 partnerships. These two points suggest looking at the team’s bat first method both against the new ball and when number six comes out to bat.

Individual batting and bowling

At a player level, R+ shows the value individuals are bringing to the team, compared to what is expected.

We all know when someone is averaging 50 with the bat they are contributing, but what if that 50 comes about through statistical anomaly (a few not outs or a big hundred followed by a duck)? Also, what about the bowler who consistently delivers a match winning performance that doesn’t show up in averages because they don’t take as many wickets?

R+ removes such anomalies.

We can do this on a game by game basis, to see who set up the victory (or who prevented the loss). This is useful post- and peri- matches because it gives unsung players credit, and reveals elements to work on.

For example, one team had a batsman this year who both opened and batted in the middle order. His R+ was -4 in the middle and +8 opening. This suggests he has potential as an opener more than a hitter down the order, and can spend the winter honing these skills if the captain decides he is going to have an opening slot.

It also gives us the ability to look at the big picture through average R+. We can quickly see how well a player contributed over the season, which often reveals more than the average alone.

One good example of this is a player for Western Warriors U16 who averaged 26 with the ball at fours. You might say this is a poor return considering the team bowling average was 12. However, R+ was just above zero, meaning when he bowled the team were “winning”. He can certainly work on improving this further but he can also take the knowledge that his performance was no worse than expected to win the match. If he was an IPL bowler he would doubtless be undervalued and could be picked up at auction at a low price yet give match winning contributions.

So, R+ works incredibly well at a variety of levels. It is not designed to replace either traditional averages or insights from experienced players and coaches, but it does throw up points for those players and coaches to build on either in practice or applying tactics. Additionally, once set up, it is easy to update even with simple scorecards to work from.

The sister of R+ is Win Percentage (Win%), which is applied in the second innings for batting and bowling. We will look at that in the next article.

Better Cricket Performance Analysis

In the last cricket season, I have been working on better ways to analyse cricket team performance beyond the results, scores and averages, especially at club and school level where the scorecards are often the only source of information.

The motivation behind this project is the too basic traditional way of analysing performance. It doesn’t allow for spotting strengths and weaknesses in a team. The result might tell you how close the game was, but not where it was won and lost. Averages only make sense if you know the context of the matches, but these contexts change every game from rain affected over reductions, to pitch quality and opposition ability. If there is video analysis – which is rare anyway – it often focuses purely on the technical and not on the outcomes.

So, my mission was to come up with a way of overcoming these obstacles to provide a reliable measure of performance in cricket batting and bowling. The goal was to give players, coaches and captains something to work with when planning practice and tactics. 

In the next few articles I will show you this method and provide some real-world examples from teams I have analysed in 2019. The first step is a more useful context.

Par, Target and Expected Scores

Context is important in matches. It’s unhelpful comparing the average scores in ODI cricket to, say, U16 regional cricket in Scotland. Scores are too different despite the matches both being 50 overs.

That’s where Par scores become useful.

I examined historical scores in several competitions including the WDCU Premier Division (WDCU) and came up with an average score for the competition, this is the Par score. For WDCU this is 189. This provided a touchstone context for scores: We know 189 is the average score between two average teams.

To create an even more granular context, we can weight Par by the ground the match was played.

For example, games at Clydesdale average 225, while games at Uddingston average 145. After weighting the Par scores are 197 and 159. So, we can be confident – based on scores from 187 matches over three seasons – where the average lies on any given ground, no matter which two teams are playing on it. If a team bats first at Ayr, for example, we know they have an even chance of winning by scoring 188.

There also will be occasions to know the Par wickets, which uses the same method but for wickets instead of runs. Here’s what that looks like:

Naturally, this only applies to batting first in limited over matches. When batting second, the score is known. This score plus one becomes the Target and is no different to usual.

Expected Runs and Wickets

At this point we know what a team needs to do, but what about bowlers and batsmen?

This depends on the information we have available, but even at the crudest level we can calculate accurately what an average player is expected to do. This is Expected Runs (xR) - conceded for bowlers and scored for batsmen - and Expected Wickets (xW).

The more runs a batsman scores above xR, the better they have done. The fewer below xR the worse they have done. The same applies for bowlers runs and wickets. This is more useful as a measure than batting average, which needs a few games to be reliable and even then, is only “the more the better”.

Calculating xR and xW is from scorecards alone has issues, but can be done to create a ball-park figure in the following areas:

• Batting first xR using Par score and average wickets.

• Batting second xR using Target score

• Bowling first xR using Par score and overs bowled

• Bowling second xR using Target score and overs bowled

• Bowling first xW using Par wickets and overs bowled

• Bowling second xW using Target wickets and overs bowled

For the bowlers, we can create Expected Average, Economy and Strike Rate (xA, xE xSR) and compare it to actual performance.

So for example, the xR for batsmen batting first at Ayr is 23.5. While a bowler who bowls 10 overs would expect figures of 2-38 (actually 1.6 wickets and 37.6 runs but rounded).

If we have access to balls faced by the batsmen we can also create Expected Strike Rate for the batsmen (xSR). Although not all cards offer the information so it can’t always be factored in.

There there is a problem with this limited method. It does not account for the stage of the game, such as batting or bowling at the death with little time left. So a bowler at the death is less likely to reach xR and more likely to reach xW. A batsman coming in in the last two overs has little hope of reaching xR (although this latter point can be overcome if we know balls faced).

These are limitations of the method enforced by only having scorecards and not scorebook information.

To deal with this, we can produce Expected scores using the DLS calculator, but it requires more information than the average scorecard gives us. However, if you have access to both the DLS calculator and over by over score updates - like in a scorebook -  you can get a more accurate Expected score.

To do this we need to compare the score at the end of any over with the DLS Par for rain affected matches. When batting second, the DLS calculator can be used as normal to generate the table (regardless of the weather). When batting first, the table can be produced using the Par score. From here, we can easily see how far the batting team total, and individual bowlers and batsmen all are ahead or behind the expected score. It is both more accurate, and takes into account the stage of the game and the wickets fallen; solving the above issue.

Once we have this basic framework information, we can start to create a more useful metric for players and coaches that the averages. Click the links below for articles on each one:

• Runs Added (R+)

• Win Percentage and Win Percentage Added (Win%+)

• Impact