Stream and scorecard

What made this wicket special is that at Lord's, Stokes was fighting to keep England alive in their chase of Australia's 285. He had scored 89 off 115 balls, and England needed his wicket to fall as little as possible. Then Starc produced an absolute beauty.

And that reaction from Stokes, dropping and kicking his bat in frustration, said everything. Even he knew Starc had produced something special.

Good areas.

Hi everyone, very long post upcoming.

I wanted to share something I have been working on, essentially a more fair way to compare test bowlers with each other.

Average has always been the definitive stat to compare bowlers, especially within test and ODI cricket. One thing the stat doesn't represent, that misses the true uniqueness of cricket compared to other sports, is how different conditions can be from era to era, country to country and even match to match. Is it fair to compare the averages of a bowler who bowled in the 2000's with one that bowled in the 80's? It's well known that conditions were strongly in favour of batting in the 2000's, so bowlers bowling then were naturally going to be disadvantaged compared to bowlers in the 80's, when conditions might have been more even. Therefore, bowlers in the 80's will likely have a lower average than a bowler in the 2000's just because it was more difficult to bowl in the 2000's.

To bring more neutrality to the discussion, it's more fair to not look purely at averages but rather how a bowler's average compared to the average of bowlers who played in the same match as them. This removes all potential points of differentiation. In the example above of comparing a bowler from the 2000's to a bowler from the 80's, while the bowler in the 2000's will likely have a higher average, that average might be much lower than other bowlers from the era compared to a bowler from the 80's. For some, it might mean that bowler in the 2000's was actually better, despite having a higher average.

We can compare Match Factors = Bowlers career average / (Average of bowlers in the same match, exclu bowlers average).

Before going further, I realize that Jarrod Kimber has done similar analysis of bowlers. I want to stress that I'm not just copying his idea. I have done these types of analysis before, such as adjusting batting averages to the years a batsman played in and grounds played in as well. I've only recently gotten the time to try a more detailed match-by-match analysis.

My goal was also to share a model that can calculate match factors for others to use and play around with.

I've attached links to two files, using data from 01/01/1970 onwards:

The files are uploaded to GitHub, to download the files click them in the link, and again, then click the download button next to 'Raw'

For both, the main stats that are shown are (in the 'Playground' sheet):

Total Match Factor: Match factor comparing averages to all bowlers who bowled in the same match

Type only: Match factor comparing averages to only the same type of bowlers who bowled in the same match (eg: fast bowlers v fast bowlers)

Type Only - opposition/same team: Type only match factor but also either only comparing against the opposition or with the players team in the same match.

You can compare match factors with the following variables:

Innings between/Date: Select if you want to look at match factor between certain innings (eg: first 50 innings) OR between Dates (eg: only 1990's) Min Wickets: Minimum wickets required to be taken between the selected innings or date

Same team/opposition: An additional option, do you want to compare the match factor with only opposition players or compare with the same team?

Type-only - same team min. balls: If 'Same team' is selected above, then what is the minimum amount of balls the players of the same team and type have to bowl compared to the bowler.

Home/Away: Select if you want to look at match factors for only home, away or all matches.For the file 'only avg with countries', you have the additional option to look at match factors against individual countries as well.

Another tool in the 'Playground' sheet is the ability to look at the 'peak' match factors. This is in the macro box at the top of the sheet:

You can select how long you think a peak duration should be, based on number of consecutive innings, and also what is the minimum number of wickets to qualify for comparison.

The results of the macro are in the 'Best Innings - Avg/SR' sheet, based on whether you want to selected the macro for Avg or SR peak. Note that for the 'only avg with countries' file, the SR macro is removed due to large file size.

Warning: The macro time can be long depending on the PC. I'm not an expert on macros so I maybe did not program it efficiently :)

I realize Excel might not be the best tool to make such a model but I don't have any programming expertise so I could not develop a similar model in python or similar, so I stuck with what I know best to try and achieve it.

Would really appreciate any feedback.

All data is taken from cricinfo statsguru. Would also like to give appreciation to for inspiring me to publish my random cricket stats work to reddit.

Features the team song at 5:30.

Renegades v Stars

Strikers v Hurricanes

PS: not in my wildest was Maddo getting a roll.

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Australia win by 214 runs.

Scorecard

Cheeks.

Revenge is sweet. A year after South Africa beat Australia in the WTC final at Lord's, Australia just handed them a 214-run thrashing at Kingsmead — Renshaw's 190 (plus 82 and two wickets for good measure), Hazlewood's career-best 6/49, and Markram's 237-ball 75 the only real resistance.

The table now: Australia 81.81 and clear at the top, New Zealand 72.22 second, South Africa tumbling from 75.00 to 60.00 and down to third.

So who actually makes the final at The Oval next June? I built a scenario calculator — pick a result for any remaining Test and the table updates live. I've pre-set it with Australia winning the two Tests left in South Africa. A few scenarios worth playing with:

• Australia win out: 90.91 PCT. Nobody catches them.
• The real decider might be the Trans-Tasman series. New Zealand's ceiling is 89.58, and the 4 Tests against Australia in Dec–Jan are basically a semi-final — if NZ take that series, the race flips.
• South Africa are damaged. They can still reach 85.71, but after Durban they don't control their fate anymore — they need favours.
• India's only lever is the 5-Test home series against Australia in Jan–Mar. Even winning all 7 remaining Tests only gets them to 70.37, so they need Australia to bleed points elsewhere too.

Play with it here: https://cricketonair.com/tests/wtc-scenarios/

It's my site (CricketOnAir) — the table updates as results come in.

Scorecard

Looks like the match bot is on the beers early. If it turns up this thread will be locked so please head on over there. Cheers.

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