We are well through the season and the final results are pretty tight to call. My standing questions still remain that the model tries to address and they are:
- will the team final standings settle on the mid-season predictions?
- how early in the season does the model give signals to the overall competitiveness of a team?
Before I give some thought to the two bullets, let’s just remind ourselves of the conceptual approach to the model that has been used this season to calculate the ‘Value‘ Coefficient. At the beginning of the season we nominally placed the teams in a predicted end of season order and then calculated the race on race Value target that each race Value Coefficient could be measured against. We could have wasted much time trying to work this out but I simplified the matter by just placing them in the order they finished the last season knowing that mid-season we could reflect on matters, re-order if necessary and then recalculate the respective targets and let it run to the season end.
Fairly early on in the season some very obvious signals were emerging which suggested the following:
- Red Bull and Force India were the only teams meeting or exceeding their Value Targets. They were originally predicted to be placed 3rd and 5th respectively come season end.
- Ferrari and Williams were consistently well under performing their Value Targets. They were originally predicted to be placed 2nd and 3rd respectively.
I was sufficiently emboldened by the time Verstappen moved to Red Bull for me to place a £50 wager on Red Bull coming 2nd in the Championship, which now seems to make more sense now than it may have back in the season!
Then came the summer break and the review. I did the following:
- moved Red Bull to 2nd and Ferrari to 3rd
- moved Force India to 4th and Williams to 5th
- recalculated the Value Target Values
- monitor as the season progressed
If I had to call it now I would say the following with some confidence:
Mercedes, Red Bull, Ferrari, Force India, Williams, McLaren…….
Why?
Mercedes does not need further explanation the only question that remains is which driver will win and that is not really the main objective of this model.
Red Bull have not only had to be moved up in the model, but looking at the results on the revised targets indicates they are more than consistently achieving a 2nd place performance. I would go further, I do not think it will even be close to Ferrari!
Ferrari despite all the hype and heritage it is clear now (as is probably has been most of the season) that they are not best of the rest. I think the ‘Casting’ phenomena will be well explored in this team going into next season. I am convinced that the car is good but that it is the mix of key individuals that has explained the overall result for this team.
Force India are not quite on top of their well deserved current 4th place yet, but I would be very surprised if this was lost by season end. This team showed glimmers of this level of performance last year but didn’t quite beat Williams as a result, but this year has clearly been different. It is so good to see a team like Force India perform so well on the budget they have and their 2nd place in our Value Chart is no surprise.
Williams always promise so much of late and have just not delivered. Like Ferrari the ‘Casting’ factor will reveal much going into next year. It is hard to believe that this team was best of the rest only a few seasons ago. What has gone wrong? Is it the car, the drivers, the senior leadership or a combination of all these things. They must be asking the same questions. Should Force India finish ahead of them come season end then it will be interesting to compare ‘Casting’ analysis as it will not be money that delivered the difference.
Look Ahead to 2017
My next blog will look at what we are prototyping for next year to move the model onwards. I will introduce the ‘Casting’ factor to the Value Coefficient which I hope will be the final step towards really understanding how the teams performance is influenced by the mix of individuals in key team posts over time.