Do BBL pitches change if the WBBL uses them first?

Earlier this week, Matthew McInerney on Twitter posted about Mark Waugh in the commentary box for a Big Bash League game (I believe the second semi-final) suggesting that WBBL matches played beforehand were producing “slow pitches” for BBL matches.

Unfortunately I didn’t see the comment, but this is something which can be analysed.

Double headers of the WBBL and BBL have been regularly scheduled throughout the last two seasons. Twenty-four out of seventy BBL games across 2015/16 and 2016/17 were scheduled immediately after WBBL matches at the same ground.

I assume that the same pitch was used for both matches in all of these double headers. So for each Big Bash League match held as part of a double header, the pitch has already been used for up to forty overs of women’s cricket that same day. At the one double header I attended, the steam roller was run over the pitch between matches, but it isn’t inconceivable that these pitches could be different.

A ‘slow pitch’ suggests to me that scores would be lower, and we can test this premise by comparing scores in matches preceded by a WBBL match, and those not preceded by such a match.

Firstly, let’s examine the total figures. My dataset doesn’t include the semi-finals and finals played in the last week, so just under 1/3 of the sample were double headers.

Double header? Matches Median runs Median balls Median run rate
Yes 21 317 235 8.18
No 45 320 234 8.35

The number of runs scored and the number of balls faced are roughly the same at the median point. The difference in the median run rate translated to 6.8 runs per match, which is slightly more than the run gap, but not much more.

Obviously this isn’t enough to answer this question. We don’t know the distribution of matches, and whether one is consistently less than another. More importantly, we don’t have a breakdown by venue. If pitches are having an impact, it seems far more plausible that there is greater difference between venues, rather than difference depending on the presence of a women’s team earlier in the day.

This first chart shows the difference between the median run rate in double headers and single matches at each of the eight grounds that hosted BBL matches in 2015/16/17.

doubleheader3.png

A larger bar indicates that run rates were higher in double headers than in other matches. What we find is that there is no difference in run rate at the Adelaide Oval and the Sydney Showground (aka Spotless Stadium) while substantially more runs were scored at double headers at the SCG and MCG. Double headers produced lower scoring rates only at Docklands, Bellerive and the WACA.

This next chart goes into a bit more detail. It shows the run rate for every match played in BBL05 and BBL06, broken down by venue. Red dots represent double headers, blue dots represent standalone matches.

doubleheader4.png

I see no clear evidence of a trend. While the highest score was at a standalone game (445 runs scored at Renegades vs Hurricanes at Docklands), there are plenty of grounds where double headers produced more runs than single matches.

The lowest performance was a standalone match at Bellerive, the Gabba, the MCG, the Sydney Showground and the WACA. Only at the SCG, Docklands and Adelaide did this honour go to a double header match.

This isn’t definitive evidence of anything, but the available data suggests to me no proof that playing a women’s match on a pitch has any impact on the scoring in the following men’s match.

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Winning the toss and chasing

Early in this season I blogged about the trend of teams choosing to bat second. Since that time that trend has become clearer with most teams choosing to bat second when they win the toss.

I’ve updated my analysis and also looked into how often teams win when batting first or second, when you break down the matches based on how close they are. In short, close matches are overwhelmingly won by the team batting second.

I’ve written up this analysis in my first cricket piece at the Guardian Australia.

And here is the most interesting chart, showing break down of results based on margin of victory in BBL06 and WBBL02:

gutoss5.png

And as a bonus for blog readers, here is the same chart for the last six seasons of the BBL and the WBBL and its predecessor:

gutoss4.png

Are scores increasing?

Last week we saw a remarkable score in the Big Bash League, where the Melbourne Renegades scored 222 (a record BBL score). The Hobart Hurricanes then met the challenge, scoring 223 in response. We’ve seen the record for the most runs scored in a match broken twice in 2016: first with 489 runs in a T20 International between the West Indies and India in Florida, and then 497 runs in a New Zealand domestic match between Central Districts and Otago.

So my question: is this a broader trend, with more runs being scored across Twenty20 cricket? In this post I’ll attempt to find out, and also look at how different ways of scoring are becoming more or less prominent.

Firstly, let’s just look at the median number of runs scored in each innings. I wanted to simplify the results, but keep together whole seasons, so each year covers the northern summer in that year and the southern summer at the end of that year, so each year covers April-March.

par1.png

The number of matches in 2003 and 2004 was particularly low, but once Twenty20 cricket became more stable we saw the median score stabilise around 150. It dropped from 2010 to 2013, but since 2014 the median score has shot up, to close to 160 in the last ten months.

I wanted to isolate where this growth is happening: is it because matches are being played in different places, or is there a general increase in scoring rates?

This next chart shows the run-scoring rates for the top-level men’s domestic T20 league in five big cricketing countries, as well as the scoring rates for T20 Internationals.

par2.png

Scoring rates are higher in Australia, India and England compared to the West Indies, South Africa and in international matches. While the data is noisy, it does appear that there has been a general spike across the last three seasons.

Secondly, I wanted to get a sense of how run-scoring has changed in terms of what sort of runs are scored. Runs can be split into four categories: fours, sixes, extras and ordinary runs scored by running between the wickets.

par4.png

Over the last decade, we’ve seen an increase in the proportion of runs which are scored as sixes, while there has been a decline in the proportion of extras and the proportion of fours. Ordinary runs remain the primary way of scoring, and this has remained roughly steady just over 40% of all runs scored.

(It’s worth noting that the drop in fours and spike in sixes is off a very small sample – 8 BBL matches, 6 matches in the NZ domestic competition, and 2 internationals between New Zealand and Bangladesh).

Finally, this chart shows the same men’s data as the above chart alongside the same data for women:

par6.png

Ordinary runs make up a much larger share of runs in women’s cricket. Fours and extras make up similar shares as in the men’s game (with four-scoring increasing over the last three years), while six-hitting is a much less important part of the women’s game.

Six games to go in the BBL – possible results

We now have only six games to go over the next week in the Big Bash League, and this means the number of possible finals arrangements is becoming much more manageable. There are 64 different outcomes depending on who wins each of those six matches.

This is the table as it stands now:

Team Played Won Lost NRR
Melbourne Stars 6 4 2 +0.786
Brisbane Heat 6 4 2 +0.557
Perth Scorchers 7 4 3 +0.331
Sydney Sixers 7 4 3 -1.062
Sydney Thunder 7 3 4 -0.097
Hobart Hurricanes 7 3 4 -0.179
Melbourne Renegades 6 2 4 -0.003
Adelaide Strikers 6 2 4 -0.157

In the case of teams on the same number of points, the net run rate will be used to break ties.

The teams have been split into four tiers. The Stars and Heat stand in a clear lead having won four matches and with two to come. The Renegades and Strikers are clearly at the bottom of the table, with only two wins, but with two matches to come. The remaining four teams have only one match left to come, with the Sixers and Scorchers on four wins and the Thunder and Hurricanes on three wins.

In this post, I’ll show the proportion of scenarios where a team would clearly make the finals, clearly miss out, or find themselves in a tie to be broken by their net run rate. It should be noted that I’m not attempting to judge the chance that each team will win any of the matches – just the possible outcomes if they win or lose.

Continue reading “Six games to go in the BBL – possible results”

Top batsmen in the W/BBL adjusted for point in the game

So yesterday I put together a blog post analysing how performance varies for batsmen based on which point in the match they come to the crease.

Today I’m going to show which players have performed the best in the Big Bash League and the Women’s Big Bash League, when their score is adjusted to reflect their opportunities.

This metric is based on this graph: how many runs does each batsman score, broken down by the over in which they entered the innings:

entry2

And here’s the same chart for women’s Twenty20 cricket:

entry6.png

The WBBL data includes the last four seasons of the state-based competition which preceded the WBBL, and it does appear that players in domestic Australian women’s Twenty20 cricket entering in the middle of the innings have performed better over the last five years. Continue reading “Top batsmen in the W/BBL adjusted for point in the game”

Better batting metrics – judging by point of entry

There’s a lot to be done to improve metrics for players in Twenty20 cricket. This summer I’ve mostly focused on team-level metrics that give you a sense of the whole game, not about the performance of individual players.

There’s a lot of focus in Twenty20 cricket on a player’s strike rate, and that is undoubtedly important. But it’s also important that a batsman is able to last a reasonable amount of time. Players who hit a six and then get out off their second ball on a regular basis will have an extremely high strike rate, but won’t be of much value to their team. So the ability to stay also has value.

When you consider this point, the old-fashioned batting average (the number of runs scored over the number of times your wicket has been taken) has value. Ideally we’d come up with another metric which can mix together these two simple measures to give a sense of the ability of a batsman to score fast but also stick around long enough to make an impact.

In this post, I’m going to focus on a particular datapoint which I think has value when making assessments of players: when they come into the match.

Not all balls are the same, and not all overs are the same. Generally matches follow a pattern where the number of runs scored speeds up as you head towards the end of the match (barring the loss of significant numbers of wickets). You can see that in this graph:

entry5.png

The powerplay covers the first six overs in Twenty20 matches. During this time teams may only place two fielders outside of the circle which marks out 30 yards from the pitch. Following the powerplay, teams may have up to five fielders outside the circle. This clearly has an impact on the game. While the first over is the lowest-scoring over of the match, runs are scored quite quickly in overs 3-6, before collapsing in over 7. It takes until around over 15 before the batting team usually surpasses the scoring rate of the powerplay, but the average number of runs scored per over exceeds nine runs for the final overs.

Obviously you would expect different performances from batsmen depending on when they enter the match. If a batsman enters earlier in the match, they are not expected to score as quickly, but they have more time to play, while those coming in later are expected to score at a faster rate but may not have time to score as many runs.

Continue reading “Better batting metrics – judging by point of entry”

Playing risky cricket

Earlier this month I blogged about how the loss of wickets shifts the chances of victory and the number of runs scored in the remainder of the innings. In this post, I’m delving into how the speed of a team’s scoring effects their chances of losing wickets.

In an ideal world we would have ball-by-ball data which would include data on what sort of shots players attempt, and I would expect you would see a trend where batsmen attempting a lot of aggressive batting (whether or not those shots turn into runs) would be at a higher risk of losing their wicket. Unfortunately we don’t have that data, so we can only rely on the total number of runs scored, and the number of wickets lost, in each over.

I have to make a few assumptions here:

  1. Teams don’t dramatically change their batting behaviour from over to over, particularly if a wicket hasn’t been lost. So if a team is batting in a risky manner in one over and this results in the team losing a wicket, they would be more likely to have been batting in a similar way in the previous over.
  2. There is a correlation between batting in a more risky way, and scoring more runs. So while we don’t have enough data to actually categorise play according to risk, we can use the run rate immediately before the loss of the wicket as a proxy.

I have over-by-over data for just over 4000 men’s Twenty20 matches.

Firstly, let’s look at every over in the data set (excluding the first over of each innings), and the number of runs scored in the previous over, to identify how many of those overs resulted in the loss of a wicket.

wicketrisk1.png

Continue reading “Playing risky cricket”

Arguing the toss

Josh Pinn on Twitter asked last night:

This is a good question, and one that is easy to answer with my dataset. In this post I’ll look at how the fashion in men’s T20 cricket has shifted towards teams preferring to bowl first, and also whether there is evidence that this works.

Firstly, let’s take a look at how often teams choose to bowl when they win the toss in the Big Bash League.

toss4.png

It’s a dramatic shift. Up until 2011/12, it was rare for teams to choose to bowl. Around 20% took the option in 2007/8 and 2008/9, but in 2009/10 a team chose to bowl first in only one out of 16 matches. We came closer to parity in 2012/13, but only a quarter chose to do so in 2013/14. In 2014/15 the team who won the toss chose to bowl in exactly half of the matches. Last season a small majority of matches saw teams choosing to bowl.

In the first five matches of this season, every team has chosen to bowl. Continue reading “Arguing the toss”

Two BBL games and a world record score

We’ve now had the first two matches of the 2016/17 Big Bash League. The first match saw the Sydney Thunder suffer a minor batting collapse in their mid-innings before setting a respectable target which was easily beaten by the Sydney Sixers. The second match last night saw the Brisbane Heat set a very high target. The Adelaide Strikers scored very fast and looked set to meet that target, but a string of late wickets saw them fall short, with the result unclear until the final few balls.

In this post I’m going to run through some random stats about these matches (no deep analysis here) and also touch on a remarkable match played yesterday in New Zealand.

Continue reading “Two BBL games and a world record score”

Losing early wickets and chances of victory

I have a general hypothesis which I will be exploring throughout a series of blog posts in this season, and it is this: batting teams start with two resources (balls and wickets), and they spend those resources as efficiently as possible to convert them into as many runs as possible. Thus Twenty20 cricket isn’t just about scoring runs as fast as possible. You want to preserve enough of your wickets to be able to play a riskier game at the tail-end of the innings “spending” those surplus wickets to give you the capacity to score at a faster rate.

In today’s post, I want to explore the role of losing early wickets in determining how many runs a team scores, and their chance of victory.

As a taste, this graph shows the likelihood of victory for teams based on how many wickets they have lost after five overs:

wicketschance11.png

Teams that have maintained all of their wickets have roughly two-thirds chance of winning. Teams that are down one wicket are still favoured to win, but the chance of victory drops gradually further. A team which has lost four wickets has less than 20% chance of winning. There have only ever been six innings where a team had lost six or seven wickets after five overs, and in all six cases that team went on to lose.

Continue reading “Losing early wickets and chances of victory”