Update on the Performance of the UK Chasers: Who’s Best Now?

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Back in 2020 I wrote a piece here about the comparative performance of UK and Australian Chasers, and I would refer you back to that piece if you want to know more about the data set I’m using for this new article. This time, I’ll only be providing an update on the relative performances of the UK Chasers alone, using data from the first ever UK Chase show back on 29 June 2009, all the way through to 24 October 2025.

SUMMARY PERFORMANCE DATA

Let’s firstly take a look at some views of the raw data by Chaser.

On the right we have a table showing the number of episodes in which a particular Chaser took part in the Final Chase, along with a number of performance metrics.

We can see here that Anne Hegerty has the highest proportion of wins, that Darragh Ennis has offered fewest pushbacks per episode, that Jenny Ryan has (narrowly) been most accurate in Final Chases, and that Mark Labbett answers quickest in Final Chase.

Shaun Wallace has the lowest win rate, offers the most pushbacks per episode, and has the lowest Final Chase accuracy.

Darragh Ennis, however, is the slowest to answer questions in Final Chase.

A moment’s thought will suggest that these performance metrics are likely to be influenced by a number of factors, perhaps foremost of which is the target set by the team for the Final Chase.

The table on the left breaks down each Chaser’s performance based on the target set, and we can see that all Chaser’s win rates decline as the target increases, most notably for Darragh and Shaun.

Darragh, however, provides few pushbacks and is relatively highly accurate when the target is low, but falls off in accuracy at a relatively large rate and increases the tempo of his answering at a slower rate than most fellow Chasers as the target increases.

Shaun offers most pushbacks and is least accurate of all the Chasers when the target is high, but he is also second quickest in providing answers.

Anne’s response to an increase in target is to offer about 80% more pushbacks but still maintain a similar accuracy by increasing the rate at which she answers questions by about 8% (from 4.8s per answer to 4.4s per answer).

Jenny is particularly impressive in that she offers the fewest pushbacks of all Chasers for large targets and actually increases her accuracy, Her speed also increases by about 8% but only to 4.6s per answer.

Mark offers over twice as many pushbacks when the target is large and sees a 3% point decline in accuracy. He does, however, answer questions fastest at about 4.3s per answer.

Paul sees a similar increase to Anne in terms of pushbacks, but shows a slightly larger decline in accuracy, albeit to have the same level as Anne. He also becomes the third fastest in terms of response time.

Another dimension that we can investigate is the number of contestants who make the Final Chase - a variable that is likely to be highly but not perfectly correlated with the target set.

We see a similar pattern of decline in performance here as we did in the previous table.

Anne dominates in terms of win rate in episodes with a single contestant in Final Chase, Darragh when there are two contestants, Anne and Paul when there are three, and Jenny when there are four.

Shaun shows the greatest decline in performance as the number of contestants increases, and Jenny the least.

Paul shows the greatest increase in response rate as the number of contestants in Final Chase increases.

(I should note that, in both this table and the previous one, Darragh has relatively few episodes in some of the categories, which means that the various estimates of his performance are subject to relatively large standard errors.

The same is true here for Jenny, but to a lesser extent, with contestant counts of 1 and 4)




STATISTICS FOR DIFFERENT-SIZED FINAL CHASE TEAMS

Before moving on to building a predictive model of Chaser performance, it’s interesting to look at performance metrics for Final Chase teams of different sizes. These appear in the table at right

You can see there that Chase teams’:

  • pushback success rate increases by about 50% for a solo team versus a team of four

  • average target increases by almost six questions for a team of four compared to a solo team

  • average prize fund more than doubles for a team of four compared to a solo team

  • win rate increases from about 10% to 40% for a team of four compared to a solo team

Interestingly, neither pushback success rates or average targets increase by much when moving from a team of three to a team of four, but the win rate increases by over one third.

PREDICTIVE MODEL

Ideally, we’d like to arrive at a measure of Chaser performance that takes into account the quality of the team that he or she has tended to face, and if we consider the average team size for Final Chase, percentage of successful pushbacks, average prize money fund, and average target set in the Final Chase, we can see that this quality has varied quite markedly across Chasers.

Mark and Paul appear to have faced the strongest teams in terms of all four metrics.

The average size of the teams that Jenny has faced in Final Chase have been very slightly higher, but those teams have set lower targets and successfully pushed back at a lower rate. The same can be said for the teams that Darragh has faced, but they have set even lower average targets.

Anne has faced targets roughly as large, on average, as Paul’s, but with a low rate of successful pushbacks.

Finally, Shaun has faced the weakest teams in terms of getting to Final Chase, and the second-weakest in terms of pushback success and target setting.

So, to account for the main sources of variation we’ll build a predictive model that incorporates the following variables:

  • The target faced

  • The total prizemoney on offer (which will reflect an additional level of pressure)

(A number of other model variations were investigated, included some with the number of contestants in Final Chase, but this model was best in terms of AIC)

The fitted predictive model, which is a binary logit designed to provide an estimate that a Chaser will be victorious given the target, the team size and the prize fund he or she faces, is summarised in the table at left.

Speaking firstly at a high level, the larger the coefficient in the log(OR) column for a Chaser, the more likely he or she is to emerge victorious for any given target or prize fund. Shaun, because he is what’s called the “reference” Chaser is assigned a value of 0 and all other Chaser values are relative to that.

The fact that all Chaser coefficients are positive tells us that every Chaser would be expected to run down any target while defending any amount of prizemoney more often than would Shaun.

The negative coefficient on the Target variable tells us that all Chasers are less likely to prevail the larger the target, and the negative coefficient on the prize fund variable tells us that all Chasers are less likely to prevail the larger the prize fund, but the very small absolute size of the coefficient tells us that the effect is very small.

To make the numbers here feel a bit more concrete, let’s work through an example where a team has accumulated $18,000 in the prize fund, and set a target of 17. The probability of each Chaser winning in this scenario are as follows:

  • Shaun: 1/(1+exp(-(7.9+0-0.43x17-0.01x18)) = 60%

  • Darragh: 1/(1+exp(-(7.9+0.45-0.43x17-0.01x18)) = 70%

  • Anne: 1/(1+exp(-(7.9+0.45-0.43x17-0.01x18)) = 82%

  • Jenny: 1/(1+exp(-(7.9+0.45-0.43x17-0.01x18)) = 79%

  • Mark: 1/(1+exp(-(7.9+0.45-0.43x17-0.01x18)) = 80%

  • Paul: 1/(1+exp(-(7.9+0.45-0.43x17-0.01x18)) = 80%

So, broadly speaking, we’d expect Anne, Jenny, Paul and Mark to prevail about 80% of the time, Darragh about 70%, and Shaun about 60%.

Using this model we can estimate each Chaser’s expected win rate for any sized target as long as we also provide an associated prize fund. For the chart at right we have calculated the average prize fund associated with each target size for a given Chaser.

We can see that Anne, Jenny, Mark and Paul have very similar expected win rates across the entire range of targets, with Paul a little less likely to win with particularly large targets. Darragh is clearly in a group of his own behind them, and Shaun in another group again.

QUALITY OF MODEL FIT

Before finishing, we should check to see how well the model fits the actual data in terms of individual Chaser win rates as the target varies.

The table at right reveals that the model consistently produces win rate estimated within 4% points of the actual observed values except for Shaun in games with large targets.

For those games, the model underestimates Shaun’s win rate by almost 10% points.

The other, largest differences are for:

  • Darragh and large targets (4% point overestimate)
    (Note that Darragh has only had 14 such episodes so far)

  • Mark and large targets (4% point overestimate)

  • Shaun and mid-range targets (3.5% point overestimate)

Overall, the model fits all Chasers and target ranges quite well, and is especially accurate for Anne, Jenny and Paul.

SO, WHO’S BEST?

If the main performance indicator for a Chaser is his or her winning rate, then it’s reasonable to use the size of the coefficients in our predictive model as the means for ordering the Chasers in terms of strength.

We therefore have the ordering as follows:

  • Anne

  • Paul

  • Mark

  • Jenny

  • Darragh

  • Shaun

This is the same ordering as we obtained in the previous blog excepting that Paul and Mark have swapped places.