Thierry Bouvet and FIA technical engineering director Thomas Chevaucher outlined the BoP process for FIA WEC 2025 ¹. It is based on recent race lap times. Here is a look at those recent races and how they influenced BoP for the Opener in Qatar. Along with a look at how Qatar’s lap times will influence it.

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Contents

1. Introduction to the Approach
   • Peak Stint and Performance
     • Definitions / Assumptions
   • Rolling Average
   • Top Speed
2. Previous Race Performance Review
   • 2024 Round 6: Lone Star Le Mans (6 hours)
   • 2024 Round 7: 6 Hours of Fuji
   • 2024 Round 8: 8 Hours of Bahrain
   • Average of the Three Rounds
   • New Car Adjustment
3. Review BoP Parameters
   • Power:Weight
   • Weight
   • Power
   • High Speed Power
   • BoP Development Summary
4. Aligning performance and BoP
   • Introduction to Charts
   • Last Three Rounds v. Qatar BoP
5. Following Race Qatar 1812km Review
6. Manufacturer Summary and Hypotheses
7. Updated Three Race Average
8. What is Success?

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1. Introduction to the Approach

Peak and Stint Performance

“It was explained by ACO competition director Thierry Bouvet and FIA technical engineering director Thomas Chevaucher that, in Hypercar, both the 10 best laps of each car and 60 percent of a car’s best laps will be taken into account when calculating the BoP, in a bid to include both peak performance and performance over a stint.” ²

The aim here is to try and replicate that, or at the least look at similar data to gauge the inputs. This can only look at half of the process; the lap time and speed trap data is available, but the modelling to assess the changes required to adjust BoP parameters are. However, the output of the process is know in the form of the new BoP tables. Some inferences can be made, albeit with caveats.

Definitions / Assumptions

“Each Car”: This could mean each individual car (e.g. #50) or it could mean combining the laps of each car type (e.g. #50, #51, and #83 Ferrari). For this the former is used; the fastest of each individual car is used as that car’s benchmark.

Each car has a slightly different set-up. It could be that a car is good in one part of the race and it’s sister car stronger in another part. Combining the laps from this could produce an unrealistic overall performance. Within each subset comparison the fastest car of that manufacturer is chosen as they were closest to the the optimum.

It should recognized that the actual approach is unknown. Although the FIA has referred to using the fastest as demonstrative of the potential before.

It should also be recongised that cars with more entries provide more data either way.

Both best 10 and 60% best: The explanation said these were combined. It does not say how. For this a simple average is assumed.

Note that combining of the Top 10 and the 60% could be from different cars. As such it does open up this review to some influence to different set-ups highlighted above. One could be set-up for quick laps, the other to manage the tyres.

These choices, could influence the result, but are unlikely to be a major driver. When reviewing the averages the differences in Top 10 and Top 60% are, unsurprisingly, well correlated.

For completeness it should be noted that Top 60% will also include the Top 10, but they will have minimal weighting; e.g. for a 300 lap race the Top 10 laps will have a 51.7% influence on the combined performance, 1/2 + 10/300 x 1/2.

Rolling Average

“BoP will be calculated based on a three-race rolling average of the previous three WEC events” ²

The rule makers are clear on the rationale for this, “it wouldn’t have been fair to use the BoP from last year’s Qatar round as a baseline, as this would fail to take into account improvements made by some manufacturers over the course of the 2024 campaign”

The last three race are from the 2024 season:

#	Race	Date	Winner	Laps
6	Lone Start Le Mans (6h)	1/9/2024	#83 AF Corse Ferrari 499P	183
7	6 Hours of Fuji	15/9/2024	#6 Porsche Penske Motorsport Porsche 963	213
8	8 Hours of Bahrain	2/11/2024	#8 Toyota Gazoo Racing Toyota GR010 – Hybrid	235

It is assumed that this is a simple average. It could be that there is weighting to more recent events.

These will be used to review the following races

1	Qatar 1812km (10h)	28/2/2025	#50 AF Corse Ferrari 499P	218
2	6 Hours of Imola	20/4/2025

Top Speed

“Top speed figures from what the rule makers called “clean laps”, meaning without a tow, are also being taken into account to define the BoP.” ²

The data includes speed trap data. This does not necessarily measure Top Speed depending on the location. However, it is the best proxy available. It is likely that the FIA have car data that gives a better view of this.

It is also difficult to remove laps where the car gained a tow. Work could be done based on sector timing and race data, but this would be difficult and remain imperfect.

Here the average Trap Speeds for the Top 20% to 60% laps will be used. Studying the Trap Speed spreads and it looks like most unusually high Speed Trap values have run off by 20%, neither have they started to really drop off by 60%. 60% aligns with the FIA’s approach to lap times. This spread will be shown for each race.

The best car for each manufacturer will be chosen. Arguably for consistency this should be the same car as the lap time average best. The potential for this to cause conflict when drawing a conclusion about manufacturer when two cars with different set-up are chosen is acknowledged. Ease, and the ability to make sure enough lap data is available has taken priority.

The most obvious flaw to this is that there are degrees of tow.

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2. Previous Race Performance Review

The next section will review the three rounds used to help determine BoP for Qatar, the first race of 2025.

2024 Round 6: Lone Star Le Mans (6 hours)

The race was won by the privateer Ferrari ⁵. The race had lots of twists and turns, contact, penalties, tyre management, …

Top 10 best laps from each car and how they compare:

There are more data points for Porsche and Ferrari because they have more cars, only one for Cadillac and Lamborghini.

Top 60% best laps from each car and how they compare:

Peak and Stint Performance Combined

Combining this data gives us the following performance input for the first race of the three race input:

·	Manufacturer	Top 10 Ave	·	Top 60% Ave	·	Combined
1	Toyota	1:52.962	(2)	1:53.813	(1)	100.00%
2	Alpine	1:52.953	(1)	1:54.041	(3)	100.10%
3	Ferrari	1:53.135	(3)	1:53.977	(2)	100.15%
4	BMW	1:53.172	(4)	1:54.122	(5)	100.23%
5	Cadillac	1:53.349	(5)	1:54.093	(4)	100.29%
6	Porsche	1:53.437	(6)	1:54.141	(6)	100.35%
7	Lamborghini	1:53.889	(7)	1:54.587	(7)	100.75%
8	Peugeot	1:54.007	(8)	1:54.828	(8)	100.91%

The standard deviation of the combined is 0.30%.

Toyota was the fastest, although Alpine showed the best peak performance.

The spread is narrow. Even the last car (Peugeot) in the list is less than 1% away from the best lap times (Toyota). The Top 6 within 0.35%.

Editorial comment: One consequence of BoP is that we now have a new calibration of what people consider is slow. 1% used to be considered close. Now it is not, rule markers and the competitor are questioned over such gaps. With the later, it isn’t helped that most teams are very professional and consistent.

Speed Trap

The Spread between best 20% (37 laps) and best 60% (110 laps) does not look to contain the most blatant tows nor the drop off of slow laps.

The trap measurements are quite discrete as the spread chart shows:

The privateer Ferrari tops the list with the best trap speed and most consistent. The Alpine is very slippery, and notably, the Lamborghini is up there, but low on the lists. Toyota, with the fastest combined peak and stint performance is the 7th fastest manufacturer.

Distilling that down to the top car for each

·	Manufacturer	20% to 60% Ave	Lap Combined Rank	Peak and Stint Ave Combined
1	Ferrari	100.00%	(3)	100.15%
2	Alpine	99.88%	(2)	100.10%
3	Lamborghini	98.91%	(7)	100.75%
4	Peugeot	98.87%	(8)	100.91%
5	BMW	98.67%	(4)	100.23%
6	Porsche	98.66%	(6)	100.35%
7	Toyota	97.50%	(1)	100.00%
8	Cadillac	97.36%	(5)	100.29%

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2024 Round 7: 6 Hours of Fuji

Porsche wins ⁶ on Toyota’s home ground.

Other than it still being very close and in the context of this current era closeness, there is quite a difference from Fuji. Cadillac has the peak performance nailed. The range of many of the manufacturers is of the order of the difference in best speed. This needs to be acknowledge for an attempt at correlation later on.

The outlier on stint performance is the single Lamborghini. As the peak performance shows this it’s retirement after 163 laps. The impact of this is shown in the lap time spreads:

Taking the top 60% of that car’s laps, rather than 60% of the race laps would help. Although this would still influence the comparison. The following needs to be acknowledged:

A car that does not complete the race distance

• has less opportunity to put in a faster laps
• conditions could mean it was on track for the faster, or slower, period of the race

The author may look to produce the data as 60% of each car. In the meantime, an override for Lamborghini. 60% distance for Lamborghini #63 was 98 laps. The average lap time difference of #63 to the other cars at the top 98 laps will be used to used to adjust the Top 60% for the others.

·	Manufacturer	Top 10 Ave	·	Top 60% Ave	·	Combined
1	Cadillac	1:31.105	(1)	1:32.280	(3)	100.00%
2	Porsche	1:31.293	(3)	1:32.147	(1)	100.03%
3	BMW	1:31.230	(2)	1:32.215	(2)	100.03%
4	Alpine	1:31.455	(4)	1:32.457	(5)	100.29%
5	Toyota	1:31.568	(5)	1:32.408	(4)	100.32%
6	Peugeot	1:31.656	(6)	1:32.719	(7)	100.54%
7	Ferrari	1:31.863	(7)	1:32.666	(6)	100.63%
8	Lamborghini	1:32.092	(8)	1:34.099 1:33.211*	(8)	101.53% 101.05%*

The standard deviation of the combined is 0.47%.

Top Speed

The same incomplete race distance effect is relevant here for the Ferrari #51, but it still ranks highest. It is possible that it had many tow laps that influenced trap speeds slower than the Top 20%. Lower down the order a similar impact is seen with Toyota.

Looking at the sister cars these times are representative. No adjustment will be made, but it is acknowledged the best cars from Toyota and Ferrari may have been better on this chart if they had completed the race distance. To a lesser extent this is true of Cadillac too.

·	Manufacturer	20% to 60% Ave	Lap Combined Rank	Peak and Stint Ave Combined
1	Ferrari	100.00%	(7)	100.63%
2	Porsche	99.46%	(2)	100.03%
3	Alpine	98.86%	(4)	100.29%
4	BMW	98.55%	(3)	100.03%
5	Peugeot	98.53%	(6)	100.54%
6	Cadillac	98.31%	(1)	100.00%
7	Toyota	97.68%	(5)	100.32%
8	Lamborghini	95.02%	(8)	101.05%

Ferrari fastest speed trap, but slowest combined after Lamborghini.

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2024 Round 8: 8 Hours of Bahrain

#8 Toyota wins ⁷ and it had the peak performance. Buemi putting in some stonkers at the beginning of the last stint on new tyres – all or nothing.

The peak performance spread of the Toyotas almost covers the entire field! They fail the convergence aim within their own team (see What is success? below).

Stint wise it is much closer with Ferrari edging it. There is still the huge spread for the Toyotas.

The 60% 141 laps is good for most. Lamborghini is dropping off at a quicker rate and this will show up in the stint performance.

There is a bigger difference in the ranking of peak and stint performance at Bahrain than the other races, and a bigger difference of peak and stint performance. Things aren’t the same every race, which makes it difficult to find correlation for the organizers.

Editor Note: This is a good thing, albeit confusing for the armchair BoPpests. . Which, in itself, isn’t a bad thing as long as it is acknowledged.

·	Manufacturer	Top 10 Ave	·	Top 60% Ave	·	Combined
1	Toyota	1:51.421	(1)	1:53.829	(2)	100.00%
2	Ferrari	1:52.266	(4)	1:53.819	(1)	100.37%
3	Porsche	1:52.252	(3)	1:53.866	(3)	100.39%
4	Alpine	1:52.158	(2)	1:54.143	(5)	100.47%
5	BMW	1:52.504	(5)	1:53.903	(4)	100.52%
6	Peugeot	1:52.795	(6)	1:54.356	(7)	100.85%
7	Cadillac	1:52.972	(8)	1:54.194	(6)	100.86%
8	Lamborghini	1:52.888	(7)	1:54.626	(7)	101.01%

The standard deviation of the combined is 0.31%.

Top Speeds

The Peugeot tops the charts here. A check of the spreads shows that it is reasonable to use. the Lamborghini is well behind. It retired after 200 laps and the more laps we consider the worse it gets, but inherently it was down all race. No adjustment will be made in the table for this race.

The Speed Trap ranking:

·	Manufacturer	20% to 60% Ave	Lap Combined Rank	Peak and Stint Ave Combined
1	Peugeot	100.00%	(6)	100.85%
2	Toyota	99.58%	(1)	100.00%
3	Porsche	98.76%	(3)	100.39%
4	Ferrari	98.60%	(2)	100.37%
5	Alpine	98.30%	(4)	100.47%
6	Cadillac	98.26%	(7)	100.86%
7	BMW	98.23%	(5)	100.52%
8	Lamborghini	94.66%	(8)	101.01%

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Average of the Three Rounds

In an attempt to show how it varies by round here is the order of combined peak and stint performance for each race:

·	COTA	Fuji	Bahrain
1st	Toyota	Cadillac	Toyota
2nd	Alpine	Porsche	Ferrari
3rd	Ferrari	BMW	Porsche
4th	BMW	Alpine	Alpine
5th	Cadillac	Toyota	BMW
6th	Porsche	Peugeot	Peugeot
7th	Lamborghini	Ferrari	Cadillac
8th	Peugeot	Lamborghini	Lamborghini

The average of all those combine peak and stint performances these three rounds:

·	Manufacturer	Round 6 COTA	Round 7 Fuji	Round 8 Bahrain	Average	Adjustment Needed
1	Toyota	1	5	1	100.11%	-24bps
2	Porsche	6	2	3	100.26%	-9 bps
3	BMW	4	3	5	100.26%	-9 bps
4	Alpine	2	4	4	100.28%	-6 bps
5	Ferrari	3	7	2	100.38%	+3 bps
6	Cadillac	5	1	7	100.38%	+3 bps
7	Peugeot	8	6	6	100.77%	+42 bps
8	Lamborghini	7	8	8	101.10%	NA
				Average	100.35%	0 bps

Summary of these averages:

• To align at the mean six manufacturers need a reduction in performance
• Two manufactures need a sizeable increase in performance
  • This is of the order or greater than the required reduction of the best
• If a manufacturer is close to the limit of the possible adjustment (close to the 1030kg minimum weight or 520kW maximum power) then this will result in reduced performance for others.

Editorial Comment: A poorly performing outlier should be considered as such. Give it the lowest weight and highest power, but do not negatively adjust the others. The current era presents this luxury as there are many manufacturers.

Top Speed

Repeating the same exercise for Speed Trap data here is the ranking by race:

·	COTA	Fuji	Bahrain
1st	Ferrari	Ferrari	Peugeot
2nd	Alpine	Porsche	Toyota
3rd	Lamborghini	Alpine	Porsche
4th	Peugeot	BMW	Ferrari
5th	BMW	Peugeot	Alpine
6th	Porsche	Cadillac	Cadillac
7th	Toyota	Toyota	BMW
8th	Cadillac	Lamborghini	Lamborghini

The average of all those speed trap ratings these three rounds:

·	Manufacturer	Round 6 COTA	Round 7 Fuji	Round 8 Bahrain	Average	Adjustment Needed
1	Ferrari	1	1	4	99.53%	-109 bps
2	Peugeot	4	5	1	99.13%	-69 bps
3	Alpine	2	3	5	99.01%	-57 bps
4	Porsche	6	2	3	98.96%	-52 bps
5	BMW	5	4	7	98.48%	-4 bps
6	Toyota	7	7	2	98.25%	+19 bps
7	Cadillac	8	6	6	97.97%	+47 bps
8	Lamborghini	3	8	8	96.20%	+225 bps
				Average	98.44%	0 bps

Ferrari are the best here. The French manufacturers are high up too. This results in these having to have a negative high speed power adjustment. For Peugeot especially this is exaggerated as it has a high base power.

New Car adjustment

New cars will, over three races, move from being benchmarked with the fastest to being set with its own performance data. It is not clear whether the benchmark data is the best car at each individual race, or the best over those three races, or just the races considered if only two or one are needed. Racecar Engineering say “it will be balanced against the best car overall until it has it’s complete data”.

It will be assumed that it is benchmarked against the top car in each race. The tables will show the ranking of the cars that have participated in all three events. The new car will not be positioned in this ranking. However the theoretical average will be shown along with the theoretical positioning.

If this is how it works then the Aston Martin will have quite a conservative BoP.

For Qatar the Aston Martin would have the following theoretical ranking:

New Car Combined Peak and Stint Performance

·	Manufacturer	Round 6 COTA	Round 7 Fuji	Round 8 Bahrain	Average	Adjustment Needed
(1)	Aston Martin	Toyota	Cadillac	Toyota	100%	-35bps

This resulted in a BoP for Aston Martin Valkyrie of

Weight	1042 kg
Power <250 kph	504 kW
Power / Weight	0.484 W/g
Power Gain >250 kph	+0.4 %
High Speed Power	506 kW

These are highlighted for future reference, to compare with any other new cars and how this develops in the next three races.

New Car Speed Trap

·	Manufacturer	Round 6 COTA	Round 7 Fuji	Round 8 Bahrain	Average	Adjustment Needed
(1)	Aston Martin	Ferrari	Ferrari	Peugeot	100%	-156 bps

The FIA/ACO may not use the same three race average approach for Top Speed. However, based on Qatar results, the Aston Martin was set conservatively.

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3. Review BoP Parameters

The three performance BoP parameters will be reviewed;

• Weight
• Power
• High Speed Power (>250kph)

A combination of the first two will also be reviewed;

• Power / Weight

Non-performance parameters

These will not be reviewed:

• Maximum Stint Energy. This will be influenced by performance. An interesting exercise might be to compare stint lengths to this.
• Stint energy Replenishment Rate. Related to Maximum Stint Energy to give a 40s stop. IMSA publish this value. FIA WEC teams use a calculator.
• Add docking time. Depends on drivetrain and maintains a relative delay between hybrid front wheel drive, hybrid rear wheel drive, and non-hybrid.
• Dry and wet weather front wheel drive deployment speed. This is a performance BoP parameter. This hasn’t changed since 2024 round 2 (Imola). Before then it was used to balance, most notably with a lower speed to aid the Peugeot before its update.

BoP of cars in consideration period. For a longer term view see regular updates on racestats.quipe43.com ⁸

Power:Weight

LMH Bahrain adjustment

Over this period, and specifically into Bahrain and Qatar there has been an increase in Power/Weight of the LMH cars. This continued into Qatar for Toyota, but was closer to static or small drop into Qatar from Ferrari and Peugeot.

Peugeot is near the limit of the maximum allowed and could increase anymore. Its power is 520kW and its weight is 1031kg, only 1kg of the lowest allowed; it has a Power/Weight of 0.50436 v. the maximum allowed of 0.50485. There is not much room for the rule makers to go without negatively impacting the other cars en masse.

LMDh Qatar adjustment

The LMDh cars, generally, had a small reduction in P/W into Bahrain and then a big reduction into the new season. Both this and the increase for LMH could reflect the improvement the LMDh teams have made in getting to grip with their cars.

Splitting this key measurement into its component parts:

Weight

Power

The LMH Ferrari had a small reduction in P/W whereas Toyota gained. This was achieved with big changes in power, up for Toyota and down for Ferrari, with an offset with weight for Ferrari.

The reduction in P/W for the LMDh was achieved through a power reduction.

High Speed Power

Alpine and Peugeot had big hits here in Bahrain and these continue.

Ferrari is hit here and BMW and Cadillac gain. Cadillac is now with Toyota at the maximum.

BoP Development Summary

• LMDh decreased P/W for Qatar
• LMH P/W increased
  • All increased for Bahrain
  • Qatar saw
    • Further Toyota increase
    • Peugeot had no scope for increase
    • Ferrari decrease, mostly offset by weight reduction

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4. Reviewing Performance and BoP Changes

Introduction to charts to show performance and BoP changes

An important aspect to consider is that this is a measurement of the average performance of the last three rounds against the change in BoP from an average of the last three rounds to the next round.

It is not just the latest BoP change from the last round to the next. If there had been a significant change at the last round this will have a large influence on any required further changes. Movements from the same race the previous year are irrelevant to the averages (unless a calendar change means it features in the average).

The above example shows what the chart would look like if all the cars receiving a similar BoP adjustment, but they had a range of average combined performance values over the last few races. Any car on the dotted line has the same BoP-change (the iso-BoP-change line).

Cars towards the right are faster. The axis is positioned at zero BoP change and the median car’s performance.

The above example shows what the chart would look like if all the cars had the same performance average over the last three races, but different BoP adjustments. Higher up the chart shows a more favorable BoP change.

For the weight change chart the y-axis will be reverse so that a weight reduction (negative value) will appear at the top of the chart. this reflects a weight reduction being good for performance.

These can identify trends, but probably will not be able to make predictions about future movements as it is multi-dimensional. Weight and power (and high speed power) all interact. It is likely that the rule makers are also relying on simulations / models to consider how they will adjust the required performance. Any change will have a knock-on. This remains an iterative process.

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Last three rounds performance v. Qatar BoP changes

Big changes had happened into Bahrain and these need to be considered for some context.

The BoP movements are very driven by platform. LMH all getting an increase, despite the (comparatively*) large performance spread. LMDh all get a reduction in P/W and are covered by a tidy performance spread. Could this be an indication that BoPping is easier for LMDh?

*We must not lose sight that even these LMH performance gaps are historically small.

The Porsche position on these charts is influenced by the 0.3% penalty that it receives for its aero upgrade.

Most cars have worse power in Qatar. Peugeot (after its Bahrain change) and Toyota (Bahrain and Qatar) being the exception.

Top Speed

Assuming that the speed trap is placed at the end of a long enough straight then it is a good indication of Top Speed.

Top Speed will be defined simply by drag and you can overcome that with power. This leads to a pretty simple correlation for the adjustment and there isn’t the need for a sophisticate simulation / model. Generally you aren’t playing one variable against another like with Power and Weight to achieve lap time. Although it is possible that the FIA/ACO are concerned a little with how the car accelerates to the maximum.

In the adjustments we see a good correlation with the speed trap data. From the above data the rulemakers have effectively used the following relationship.

1% change in Top Speed needs 0.72% change in High Speed Power.

(assuming trap speed is top speed)

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5. Following Race Qatar 1812km Review

2025 Round 1: Qatar

Ferrari claims an historic 1-2-3. ⁹

Top 60% is closer than the Top 10.

The combined peak and stint performance has Ferrari ahead of BMW with a similar gap to Toyota and Cadillac. The combined ranking is the same as the Top 10. Peugeot lose a bit when it comes to stint performance.

·	Manufacturer	Top 10 Ave	·	Top 60% Ave	·	Combined
1	Ferrari	1:52.172	(1)	1:53.583	(1)	100.00%
2	BMW	1:52.373	(2)	1:53.737	(2)	100.17%
3	Toyota	1:52.692	(3)	1:53.774	(3)	100.35%
4	Cadillac	1:52.738	(4)	1:53.800	(4)	100.38%
5	Peugeot	1:52.911	(5)	1:54.257	(7)	100.69%
6	Alpine	1:53.077	(6)	1:54.146	(5)	100.71%
7	Porsche	1:53.192	(7)	1:54.229	(6)	100.81%
8	Aston Martin	1:53.372	(8)	1:55.091	(8)	101.32%

The standard deviation of the combined is 0.39%.

Cadillac had quite a self inflicted disrupted race, which may have contributed to a lower rank, especially as they are so close to Toyota.

Considering the relationship in LMH, Ferrari move from sixth to first in the combined rankings. It is notable that Toyota gets the biggest increase in Power/Weight, but dropped from having the best combined lap times to third. Relative to Ferrari it was 0.27% ahead to 0.35% behind. Against the other LMH, Peugeot, it went from 0.66% ahead to 0.34%.

Despite those power drops the LMDh cars remain competitive. Porsche fell to 7th, but it should be remembered that there is a 0.3% new aero penalty.

Top Speeds

·	Manufacturer	20% to 60% Ave	Lap Combined Rank	Peak and Stint Ave Combined
1	BMW	100.00%	(2)	100.17%
2	Ferrari	99.09%	(1)	100.00%
3	Peugeot	98.94%	(5)	100.69%
4	Alpine	98.89%	(6)	100.71%
5	Porsche	98.86%	(7)	100.81%
6	Toyota	98.82%	(3)	100.35%
7	Aston Martin	98.52%	(8)	101.32%
8	Cadillac	97.60%	(4)	100.38%

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6. Manufacturer Summary and Hypotheses

(ranking of the previous three races to Qatar’s combined peak and stint lap times, and speed trap data)

Alpine (Combined 4th, Speed Trap 3rd)
Alpine were 6th in the rankings for Qatar, and drop to there in the revised last three as their strong COTA combined drops out of the averaging. This kind of ranking changes will happen a lot with this method because the gaps remain so close.

Aston Martin (benchmarked to 1st)
Before the race it was benchmarked against the best car for three rounds. In the next round its average will be two round of best car and one round of worst. It could get a decent BoP adjustment. There is scope to adjust weight and power (including high speed power). In absolute values they are close to the mean in both situations.

BMW (Combined 3rd, Speed Trap 5th)
Speed Trap speeds were very impressive in Qatar, as was the overall pace. Small gain in relative pace to average, jumps up the three race average ranking. They are strong in IMSA at the moment too. This is a team that appears to be making consistent small gains – that is a good approach in the current era. Does Dave Brailsford run this team, do they have rounder wheels?

Cadillac (Combined 5th, Speed Trap 7th)
New team and great performance that potentially appears worse on the ranking due to the, er, disrupted race. Remain 5th in overall ranking, but could be underperforming.

Ferrari (Combined 6th, Speed Trap 1st)
The Ferrari was the sixth fastest, albeit close to the median, but clearly it was the fastest in the speed trap. It was only 4th in Bahrain speed trap data, but back to the top in Bahrain despite the high speed power reduction.

Potentially this lead to the rule makers reducing the power (overall, not just the high speed power) and not wanting to hinder overall they reduced the weight. Despite it having the worst P/W development of the LMH cars the low weight helped at Qatar.

Peugeot (Combined 7th, Speed Trap 2nd)
They are at the maximum power and 1kg off the minimum weight. There is not much room for the rule makers to go without negatively impacting the other cars en masse. The high power results in the need for a negative high speed power adjustment.

Porsche (Combined 2nd, Speed Trap 4th)
The -0.3% new aero penalty for Qatar and they were 0.33% off the average of those above in combined performance. Does that mean there was little aero benefit from the upgrade? This penalty will reduce to -0.2% for Imola.

Toyota (Combined 1st, Speed Trap 6th)
They were the fastest combined car in COTA, 5th in Fuji, fastest in Bahrain, and 3rd in Qatar. Fastest in Imola? Great race run in Qatar leading to consistent performance. They are at the maximum 520kW power for high speed, but their trap speed is low. A little like the Peugeot there is no where to go here.

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7. Updated three race average

Combined Peak and Stint Performance

·	24.6 COTA	24.7 Fuji	24.8 Bahrain	25.1 Qatar
1st	Toyota	Cadillac	Toyota	Ferrari
2nd	Alpine	Porsche	Ferrari	BMW
3rd	Ferrari	BMW	Porsche	Toyota
4th	BMW	Alpine	Alpine	Cadillac
5th	Cadillac	Toyota	BMW	Peugeot
6th	Porsche	Peugeot	Peugeot	Alpine
7th	Lamborghini	Ferrari	Cadillac	Porsche
8th	Peugeot	Lamborghini	Lamborghini	Aston Martin

The average of all those combine peak and stint performances these three rounds:

·	Manufacturer	24.6 COTA	24.7 Fuji	24.8 Bahrain	25.1 Qatar	Average	Adjustment Needed
1	Toyota	1	5	1	3	100.22%	-18 bps
2	BMW	4	3	5	2	100.24%	-16 bps
3	Ferrari	3	7	2	1	100.33%	-7 bps
4	Porsche	6	2	3	7	100.41%	+1 bps
5	Cadillac	5	1	7	4	100.41%	+1 bps
6	Alpine	2	4	4	6	100.49%	+9 bps
7	Peugeot	8	6	6	5	100.69%	+29 bps
	Aston Martin	NA	NA	NA	8
	Lamborghini	7	8	8	NA	NA	NA
					Average	100.40%	0 bps

New Car Combined Peak and Stint Performance

Aston Martin had such a relatively poor performance that it offsets the two theoretical best performances from Fuji and Bahrain.

·	Manufacturer	24.7 Fuji	24.8 Bahrain	25.1 Qatar	Average	Adjustment Needed
(6)	Aston Martin	Cadillac	Toyota	Aston Martin	100.44%	+4 bps

Speed Trap

Repeating the same exercise for Speed Trap data here is the ranking by race:

·	24.6 COTA	24.7 Fuji	24.8 Bahrain	25.1 Qatar
1st	Ferrari	Ferrari	Peugeot	BMW
2nd	Alpine	Porsche	Toyota	Ferrari
3rd	Lamborghini	Alpine	Porsche	Peugeot
4th	Peugeot	BMW	Ferrari	Alpine
5th	BMW	Peugeot	Alpine	Porsche
6th	Porsche	Cadillac	Cadillac	Toyota
7th	Toyota	Toyota	BMW	Aston Martin
8th	Cadillac	Lamborghini	Lamborghini	Cadillac

The average of all those speed trap ratings these three rounds:

·	Manufacturer	24.6 COTA	24.7 Fuji	24.8 Bahrain	25.1 Qatar	Average	Adjustment Needed
1	Ferrari	1	1	4	2	99.23%	-41 bps
2	Peugeot	4	5	1	3	99.16%	-33 bps
3	Porsche	6	2	3	5	99.03%	-20 bps
4	BMW	5	4	7	1	98.93%	-10bps
5	Toyota	7	7	2	6	98.69%	+13 bps
6	Alpine	2	3	5	4	98.68%	+14 bps
7	Cadillac	8	6	6	8	98.06%	+77 bps
	Aston Martin	NA	NA	NA	7
	Lamborghini	3	8	8	NA	NA	NA
				Average		98.82%	0 bps

New Car Speed Trap

·	Manufacturer	24.6 COTA	24.7 Fuji	25.1 Qatar	Average	Adjustment Needed
(1)	Aston Martin	Ferrari	Peugeot	Aston Martin	99.51%	-68 bps

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8. What is success?

As defined by the FIA/ACO: “While “100 percent convergence” is being targeted, Bouvet stated that there will still be a “natural” performance window of around 0.3-0.4 percent, which is a reflection of the average speed differential between cars of the same manufacturer.” ²

Peak and Stint Performance Combined Standard Deviation

24.6 COTA	24.7 Fuji	24.8 Bahrain	25.1 Qatar
0.30%	0.47%	0.31%	0.39%

This new averaging approach only sets the degree of “convergence” that is required. What is exactly needed still requires simulation work and lots of more detailed car performance data.

This will be influenced by many other factors; weather and the track to name but two. Primary performance gains from car developments are somewhat nullified by the three race average, but how these developments influence the performance under different weights and power will continue to skew the model.

Improving this model is an iterative process. There are only eight chances to iterative each year.

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Equipe43
It would be great to hear any comments, suggestions, errors, etc…
Use the comments below, or discuss on 10-10ths.

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References

1. FIA WEC BoP Process Explained (2025), Equipe43, 27/2/2025 ↩︎
2. Revised Balance of Performance Methodology Revealed, Sportscar 365, 26/2/2025 ↩︎
3. Revised Balance of Performance Methodology Revealed, Sportscar 365, 26/2/2025 ↩︎
4. Revised Balance of Performance Methodology Revealed, Sportscar 365, 26/2/2025 ↩︎
5. #83 Ferrari Holds On To Win Lone Star Le Mans, Daily Sportscar, 1/9/2024 ↩︎
6. Porsche Wins Drama-Filled 6H Fuji, Daily Sportscar, 15/9/2024
   ↩︎
7. Toyota Wins 8H Bahrain As Hypercar Titles Decided In Dramatic Finale, Daily Sportscar, 2/11/2024 ↩︎
8. Prologue (Qatar) BoP Hypercar update. Equipe43, 27/2/2025 ↩︎
9. Historic 1-2-3 For Ferrari In Qatar 1812km, Daily Sportscar, 28/2/2025 ↩︎
10. Revised Balance of Performance Methodology Revealed, Sportscar 365, 26/2/2025 ↩︎

BoP Bulletins

WEC_2024_D48_Hypercar_BOP
WEC_2024_D53_Hypercar_BOP
WEC_2024_D56_Hypercar_BOP
WEC_2025_D09_Hypercar_BOP_Amended

Race Results

Example average calculation: COTA

·	Manufacturer	Top 10 Ave	Top 10 Ave (of best)	Top 60% Ave	Top 60% Ave (of best)	Combined Top 10 Top 60%	Combined (of best)
1	Toyota	1:52.962	100.01%	1:53.813	100.00%	100.004%	100.00%
2	Alpine	1:52.953	100.00%	1:54.041	100.20%	100.10%	100.10%
3	Ferrari	1:53.135	100.16%	1:53.977	100.14%	100.15%	100.15%
4	BMW	1:53.172	100.19%	1:54.122	100.27%	100.23%	100.23%
5	Cadillac	1:53.349	100.35%	1:54.093	100.25%	100.30%	100.29%
6	Porsche	1:53.437	100.43%	1:54.141	100.29%	100.36%	100.35%
7	Lamborghini	1:53.889	100.83%	1:54.587	100.68%	100.75%	100.75%
8	Peugeot	1:54.007	100.93%	1:54.828	100.89%	100.91%	100.91%
		A	A=1:52.953 B=100%	C	C=1:53.813 D=100%	E=(B+D)/2	E=100.004% F=100%