Dan Weston

What would any player score in any league around the world? It's time to translate T20 numbers between leagues... A batter striking at 150 in the T20 Blast and a batter striking at 150 in the IPL have not done shown the same level of ability, despite baseline numbers (e.g. a basic internet search on stats websites) suggesting that they have. Anyone with a brain knows this, but having worked with many teams, almost every time I see a player shortlist, you still see these raw numbers anyway. Plus, no one really knows how to translate these numbers efficiently. I’ve seen this first-hand with many teams. In pre-auction videos I’ve seen on YouTube, there has been evidence of specific discussions on this subject, where I saw a specific team want to ignore the numbers from a certain league because they don’t think it’s a very high standard. Wouldn’t their approach be far more efficient if they could take into account the difference in standard or conditions instead? With the WPL and IPL auctions upcoming, understanding this is extremely critical. For example, ahead of the IPL auction, what would a batter playing in state leagues be expected to perform at in the IPL? So, I built a translator. For both men’s and women’s players, it takes every batter and bowler’s worldwide outputs (typically using a rolling two-year time period) and answers one vital question: what would their numbers look like if this player had produced them in another league? I’m going to be deliberately pretty vague about this but here is some insight into how it works - many players play in multiple leagues at multiple venues. You can measure the differences. Of course, some leagues rarely or ever meet. That’s resolvable too. Each ball is not equal. The value of a wicket on the first ball of a team’s innings or the last ball of a team’s innings is not the same. Of course, the value of a six (as an example) isn’t the same in the first ball or the last ball either. It all has to be accounted for. Each opponent is not equal either. A death overs ball bowled by a low-level associate player is not the same as a death overs ball bowled by Jasprit Bumrah. This also needs to be accounted for. Of course, taking account of all of these factors are useful, but they also need to align with ‘what wins’. Drivers of success can significantly vary by league, and that’s important. Because of this, there are many examples of the playing style for someone translating far better to one league than another - this is also taken care of with these league translation outputs. It's important to reiterate that this is a rating of expected performance in each league around the world. While it’s going to, in some way, take into account these factors, it can’t overtly see technique, fitness or personality. Those factors are, of course, also important, which is why I use my extensive network of contacts, as well as other research mechanisms, to find out more about this type of thing when I’m interested in a player. What I would say, though, that if a player has a decent sample size of data and rates badly by this model and a coach or owner wants to sign them, or worse, pay big money for them, there is basically no logical on-pitch performance based reason why they should. Who is it for? Anyone who is interested in judging a player on numbers from a league they don’t watch every week. That’s probably anyone reading this! Top tier franchise league recruitment - the obvious first answer. My translator can put every player on one scale, so as a theoretical example, a batter from the Tamil Nadu Premier League, the Sher-e-Punjab League and the Karnataka Premier League could all be compared to see which one is most likely to perform best at IPL level. If you want, you could even compare these state league players to a batter who might have only played the T20 Blast, the Big Bash, the CPL, SA20 or even the Top End T20 in Australia, or somehow a combination of all of these leagues. Everything is possible. Counties and lower tier franchise leagues - overseas signings for these league are often made on old reputations, a highlights reel or an agent recommendation. Instead, these teams can use this tool to give a quick assessment on whether a player’s output will survive the move, and what it’s likely to yield in terms of benefits or costs. Alternatively, as any team in any league can obtain, these teams could also just source a list of players, sorted in order of likely success, who are expected to perform best in their competition. National selectors and associate boards - T20 international tiers from this model prove incredible insight when you factor in the level of difficulty that each team faces. Japan played India recently - a player from Japan who played well against India will have their rating improved far more than if they had played well against a team 30th in the international rankings. This matters hugely when picking a squad or judging a player’s long-term potential. Agents for players - this tool provides tangible evidence of the value of a player. This can help with setting base prices, contractual/retention negotiations, and marketing. A value added approach like this is a far easier case for an agent to make to a franchise, as opposed to simply quoting a raw strike rate from a league the franchise doesn’t really know much about. Players themselves - I’ve sent examples to some players that I know well, and who would be really interested in this. They have loved it, and given me positive and valuable feedback. It shows them their own value and where it sits in various leagues, and the best stages in matches where to maximise their skills. Journalists and broadcasters - Every signing, auction and squad announcement prompts the same question - has the team made the right decision? The translator gives a number to help answer this question immediately. The imminent introduction of my team comparisons (illustrated by the current outputs of the XIs who played in this year's IPL final) will also be of interest. Anyone interested in learning more about how I can help them with these tools can get in touch via [email protected]. In the next 24 hours, I’ll be accepting the best requests from anyone! Simply tell me this information and I’ll post the player card. 1) Player name 2) League you want to see their data translated to 3) If the player is an all-rounder, tell me if you’d like to see their batting or bowling ratings Also, I’ll regularly post the biggest movers in the ratings, both for improving and declining players. If I don’t get another job in the IPL before the auction, I’ll publish content showing players the model likes, and much more. As mentioned, anyone interested in learning more about how I can help them with these tools can get in touch via [email protected].

Full Monte Carlo simulator results (100,000 seasons) for the current IPL squads (will explain methodology in article over the weekend) - obviously trades & the auction will change things but this is currently where we stand. RR/RCB the best chances. DC with A LOT of work to do

Did a Monte Carlo simulator (100,000 seasons) for the current IPL squads (going to write about this in detail over the weekend), one simulator with the CSK/SRH trades and one simulator without it. CSK's chances of success after the trades have dropped...

IPL team model before/after the CSK/SRH trades yesterday. Livingstone boosts CSK's death hitting rank from 9th to 7th, with slight hit to SRH's death hitting rating. However, SRH really benefit from the two extra pacers. Gone from 10th rank in PP to 5th & slightly improved their death rating as well. CSK really have helped them a lot.

Conflicted on this. 25% of a team’s budget is too much for one player if they want to build squads optimally. However the IPL has an extremely low player salary to revenue %, the players are the product and therefore they deserve more money.

Built a T10 predictor model today ahead of the start of the tournament. Caveats: 1) Squads very likely to change at short notice 2) PCB NOC situation (see second picture) complicates things hugely UAE Bulls & Emirates Eagles look the best squads. United Tigers - not so much...