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Bristol statement: £5.5million loss confirmed in 2023/24 report

Bristol boss Pat Lam (fourth from right) rallies his Bears at Exeter in October (Photo by Dan Mullan/Getty Images)

Title-hunting Bristol have confirmed a pre-tax loss of £5.5million in their latest annual financial report. Currently in second place in the Gallagher Premiership heading into Saturday’s round 10 trip to Saracens, Pat Lam’s Bears have made public its results for the 2023/24 year, a pre-tax loss that was an increase of £500,000 on the previous year.

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A statement accompanying the release of the 40-page report read: “Bristol Rugby Club Limited made a pre-tax loss of £5.5m for the 2023/24 financial year.

“The results for the year ending June 30, 2024, compare to a pre-tax loss of £5m for the 2022/23 financial year and reflect revenue challenges across the board, with less home competitive fixtures and reduced distribution income from Premiership Rugby Limited.

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“The club undertook a number cost-cutting measures during the year in efforts to reduce its operating loss. A significant focus on homegrown academy players and operating efficiencies led to a £2.8m reduction in costs.”

Bristol Sport Group CEO Gavin Marshall said: “Recording such a significant loss demonstrates that there is a lot of work left to do, although the club has taken a number of effective measures to reduce costs across the organisation.

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“We continue to be extremely thankful to the Lansdown family for their ownership, particularly at a time of continued losses and economic uncertainty.”

  • Click here to read the entire 2023/24 Bristol Bears financial report

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1 Comment
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Bob Salad II 90 days ago

I'm sure I'm overlooking significant pieces of the picture, but Bristol probably have some of the highest gate receipts in the Prem. and one of the smallest squads, so reporting such a large loss is pretty alarming.

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JW 2 hours ago
Why Les Kiss and Stuart Lancaster can lead Australia to glory

It is now 22 years since Michael Lewis published his groundbreaking treatise on winning against the odds

I’ve never bothered looking at it, though I have seen a move with Clint as a scout/producer. I’ve always just figured it was basic stuff for the age of statistics, is that right?

Following the Moneyball credo, the tailor has to cut his cloth to the material available

This is actually a great example of what I’m thinking of. This concept has abosolutely nothing to do with Moneyball, it is simple being able to realise how skillsets tie together and which ones are really revelant.


It sounds to me now like “moneyball” was just a necessity, it was like scienctest needing to come up with some random experiment to make all the other world scholars believe that Earth was round. The American sporting scene is very unique, I can totally imagine one of it’s problems is rich old owners not wanting to move with the times and understand how the game has changed. Some sort of mesiah was needed to convert the faithful.


While I’m at this point in the article I have to say, now the NRL is a sport were one would stand up and pay attention to the moneyball phenom. Like baseball, it’s a sport of hundreds of identical repetitions, and very easy to data point out.

the tailor has to cut his cloth to the material available and look to get ahead of an unfair game in the areas it has always been strong: predictive intelligence and rugby ‘smarts’

Actually while I’m still here, Opta Expected Points analysis is the one new tool I have found interesting in the age of data. Seen how the random plays out as either likely, or unlikely, in the data’s (and algorithms) has actually married very closely to how I saw a lot of contests pan out.


Engaging return article Nick. I wonder, how much of money ball is about strategy as apposed to picks, those young fella’s got ahead originally because they were picking players that played their way right? Often all you here about is in regards to players, quick phase ruck ball, one out or straight up, would be were I’d imagine the best gains are going to be for a data driven leap using an AI model of how to structure your phases. Then moving to tactically for each opposition.

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