From Reddit, Discussing My Decade of Refereeing Analysis

"A Big Data Analysis of Paul Tomkins’ Decade of Referee Research"

The following AI summary (below this introduction) of one of my officiating articles, shared on Reddit yesterday, is exactly what I have recently been trying to do with my own work via Notebook LM, which is a self-contained AI research tool where you feed in your own work (or that of others) to find summaries or patterns.

I don’t normally read Reddit (I spend enough time reading and commenting on here!) but the link was shared on TTT, so I had a look.

I’m not a fan of a lot of AI in general (and don’t use chatbots), especially when it comes to creative writing and other arts, but to summarise your own work seems a good use, not least as I can then look to spot any mistakes or hallucinations.

As I’ve been wanting to clearly summarise the dozens of pieces I’ve written over the years, I am currently trying to do what this Reddit user has done, only I am doing so with far more of my data and I’ll spot any mistakes.

I’ve already fed various articles in, but it will take time to check the results. I also need to standardise the formatting on the data not yet included. But it seems a powerful tool.

There’s also an outrageous, scandalous, unbelievable suggestion by the OP on Reddit that I waffle on, and I’m so outraged by this I’m tempted to spend 37,000 words expressing how wrong that is. (Okay, point taken.)

But at least give me 500 words to explain.

I signed up to Reddit via my Google account to briefly reply to ask how the data was created, but instead of appearing as Paul Tomkins as expected got given the auto-generated name “No-Income-4523”. Cheers! (I don’t make a fortune but I certainly make an income from TTT!).

But trying to explain the data is the hard part. That’s where the waffle comes in.

We can all just share numbers, and they could be from thin air. So you need to explain. But that then takes away from the impact of the data. Using footnotes and bookmarks seems the way forward.

I’ve got gigabytes of data in countless spreadsheets, but they are all in different formats covering different metrics over time periods.

The more issues you have to point out, the harder it is to explain it without it getting unwieldy; yet it is the very notion of a greater weight of circumstantial evidence that makes a circumstantial case.

All the direct evidence we have so far is one (of surely many) examples of a ref caught on camera. Not all refs will be that stupid, careless or hateful, albeit Rodger Gifford is another disgraced PGMOL member. (And plenty will be fine, normal and upstanding, but still at the mercy of the PGMOL and its culture of self-protection.)

I try to get away from this stuff, but it keeps coming back, as there’s just consistently apparent biases in the PGMOL officials, that to me, shows up in their data. But my plan is to produce something like the Reddit comment below, in as readable a format, only with a greater amount of data. No one else seems to be trying to analyse the data in all aspects of officiating.

I’ve always hoped someone would, but it seems you might get a simple analysis of a team’s win percentage under certain refs, when they could just mean they did you home-bankers against the fodder.

The PGMOL may continue to gaslight us all (classic case again, as expected, with Howard Webb and the hardened Paxman-like journalist, er, Michael ‘Soft Questions’ Owen), but it’s the deep data that will show trends; not all the ‘one-off’ examples that they never group together, and thus write off as subjective calls that *just happen* to go against the Reds at an alarming rate.

I also have my latest three-year study of fouls won and Foul Balance to incorporate, where on this metric, Liverpool are massive outliers, with only Man United, in this data, fairly close by, with the two Red Giants in orbit well away from the rest of the Premier League.

So what follows below is that Reddit post, to which I hereby add my own disclaimer in that this is appears generally correct to my eyes – but as the OP notes, there are some inconsistencies which, to me, are down to all the different time periods covered (some of the penalty data is older, for example, with Liverpool doing pretty well for penalties last season and terribly again, once more, this season), which I will make clearer when I do my own version, which will also be more up to date.

The OP has tried to add his own clarifications. I’ve added just one that stood out: [PT: This changed with -1 Liverpool and +1 Man City on Sunday].

Again, this is an example, to show what can be produced. And my version will be checked for greater accuracy.

I will do a fuller, more thorough and thoroughly checked version, and I will look for the stats-geniuses, professors and academics on TTT to help me verify the results.

(Apologies if that is waffle, but I wanted to explain the ‘good process’.)

Right, over to Reddit:

https://www.reddit.com/r/LiverpoolFC/comments/1ou56bh/a_big_data_analysis_of_paul_tomkins_decade_of/

A Big Data Analysis of Paul Tomkins’ Decade of Referee Research

r/LiverpoolFC•1d ago

davyp82

Here’s a summary of Paul Tomkins’ excellent research. Some of it is from 2019 onwards, some of it from 2015. The odds of all these anomalies happening randomly together appear to be in the tens of millions to one.

EDIT: DISCLAIMER: I’ve had a little more time to review this and edit a couple of inconsistencies or sections that lack clarity, as this is an AI generated summary of Tomkins work. Personally, I think he is a great researcher, but getting straight to the point isn’t his strength and most people won’t wade through the significant amount of waffle he writes before getting to the graphs (no offence if you’re reading this Tomkins! Great job, all things considered!). I don’t claim there won’t be the odd mistake in the summary, but please see the above link for yourselves with all the graphs if you want to delve deeper. I also don’t claim he himself hasn’t made mistakes in his analysis or data collection. I won’t have any answers for you if there are errors because it is not my work.

Liverpool – Disadvantaged

• Their balance of penalties for vs against per 1,000 penalty-area touches are roughly 4.0 for vs 7.6 against (net -3.6), ranking 24 of 27 clubs.

• Despite spending more time attacking in the opposition area than nearly anyone else, Liverpool’s games-per-penalty ratio is among the highest (worst) in the league.

• Liverpool have the smallest positive VAR swing among top clubs (+2 overall 2019-2024).

• Their subjective VAR penalty decisions are negative (2 for - 3 against) while City have +9. [PT: This changed with -1 Liverpool and +1 Man City on Sunday]

• VAR interventions in Liverpool’s favour happen later in matches on average than those against them, indicating less timely correction of mistakes.

• Liverpool went over seven years without an opponent receiving a second-yellow red card in a match against them. Every other team saw this happen to their opponenets at least 5 times in that period. Liverpool’s opponents zero second yellows over this timeframe is in the 1 in 1,200 to 1 in 27,000 chance range.

• Under certain referees (e.g., Coote, Atkinson, Tierney, Hooper), Liverpool’s rate of favourable “big decisions” is consistently negative and their win rate falls below statistical expectation.

• In aggregate over eight seasons, Liverpool’s deficit in big decisions vs expected equates to roughly 30 to 35 net incidents (≈ -12 to 15 league points).

Manchester City – Favoured

• Manchester City have won ~38 % more penalties than Liverpool under Klopp despite scoring only ~16 % more goals overall.

• City and Liverpool have similar attacking metrics, yet City have about three times as many penalties. EDIT FOR CLARITY - This refers to per touch in the box. So, 38% more absolute number of pens, but 3x as many per touch in the box

• City’s net VAR penalty balance is the league’s best at +9 (10 for, 1 against).

• City players are rarely sent off in domestic competition; Michael Oliver has officiated ~50 City matches without a single City red card. There is a less than 0.1% chance that this could happen randomly over the same period as another team (Arsenal, funnily enough) getting 8 red cards from him.

• City frequently receive lenient treatment on fouls and yellow-to-red thresholds, maintaining 11 players in situations where others would be dismissed.

• City’s “big decision” balance is consistently positive across all referees and seasons examined.

• Some refs (e.g., Anthony Taylor, Paul Tierney, Michael Oliver) show favourable outcomes for City and have no comparable negative anomalies.

• Combined penalty and VAR advantages give City an estimated +25 to +30 incident swing (≈+10-12 league points)over the same period, meaning a 55 - 65 incident swing vs Liverpool (≈ 22-27 league points). A reminder that two of City’s titles were won by a single point.

Manchester United – Historically Favoured

• United top the league in net penalties per touch (+5.2 difference) and have the most positive “big decision” balance since 2015.

• They receive more penalties for, fewer against than any other major club.

• Under VAR, United saw many foul calls reversed against them (17 vs 5 for) but remain net positive over the long term.

• Certain referees from Greater Manchester areas statistically award more penalties and fewer cards to United than to visiting sides.

Arsenal – Moderately Disadvantaged *but severely disadvantaged from 2022-24 (surprise, once they rivalled City)

• Arsenal’s penalty-touch ratio -1.8 ranks near the bottom half of the league (≈ 17th), implying fewer penalties than expected for their attacking volume.

• Michael Oliver has shown eight red cards to Arsenal players in ≈ 55 matches (no other top club comes close).

• Arsenal often record more cards and fouls than opponents in the same fixtures under identical referees.

Chelsea – Favoured

• Chelsea show a positive penalty differential (+3.5) in the 2015–21 data.

• They hold a net positive VAR swing (+5 to +6), similar to Manchester clubs.

Tottenham Hotspur – Slightly Disadvantaged / Neutral

• Spurs’ data are roughly neutral but trend slightly negative in penalty frequency relative to possession and box touches.

• No sustained advantage is evident; they fall between Arsenal and City in overall benefit.

Summary of Club-Specific Effects

• Most favoured overall: Manchester City (since VAR) and Manchester United (historically).

• Moderately favoured: Chelsea.

• Neutral or slightly negative: Tottenham.

• Disadvantaged: Arsenal (but if you isolate 2022–23 onward, Arsenal move from “disadvantaged” to “severely disadvantaged” in subjective**, outcome-swing decisions.**

• Severely disadvantaged: Liverpool.

Together, these results outline a persistent directional bias favouring the Manchester clubs (especially City in the VAR era) and disadvantaging Liverpool more than any other elite side, along with Arsenal since 2022.

Now let me remind you that refs have worked for megabucks in the one country that is run by City’s owners, with Mansour’s brother being the president of the UAE league too.

[End of Reddit post.]