Formula One

Engineering a Successful New Car: Starting a New F1 Season with McLaren Racing


The 2022 season ignited a world of changes for McLaren Racing and Formula 1 with the biggest reengineering in modern F1 history. Each team now has a budget cap, and significant rule changes have been introduced, altering strategies and adding excitement for the fans. Another change that we're thrilled about is that DataRobot is one of McLaren's newest partners. As part of this relationship, DataRobot will be integrated into the McLaren Racing infrastructure, delivering AI-powered predictions and insights to maximize performance and optimize simulations. DataRobot is collaborating with team members across departments of McLaren and showing off co-marketing activities, including logos on the McLaren's MCL36 race cars and on the race suits of McLaren F1 drivers, Lando Norris and Daniel Ricciardo.

In 'F1 Manager 22,' races thrill but off-track play feels like a chore

Washington Post - Technology News

Through it all, though, I couldn't help but wish that the game had additional gameplay modes to spice things up and make it more accessible for casual fans. A quick-play option to call strategy on a one-off race weekend -- at any of Formula 1's global tracks -- would have been a wonderful addition, instead of having to follow the actual race schedule through career mode. Creating your own team from the ground up, a feature available in the Codemasters series, also would have been a nice touch. And there are no multiplayer options whatsoever, which is a real shame, since I would love to wage battle against some of my friends (especially since the AI tends to be so vanilla).

You probably know someone who just got into Formula One

Washington Post - Technology News

De Rochefort is one of those F1 initiates eager to integrate her latest fascination into her lifelong gaming hobby. That being said, what she likes most about the sport -- the characters, the grudges, the meta-narratives surrounding every hairpin turn -- is not easily replicated in a racing series. She's more excited about the forthcoming "F1 Manager 2022" from Frontier Games, a spiritual sequel to 2000′s "F1 Manager" and the first officially licensed F1 management simulation to come out in over 20 years. It's a game that allows players to fine-tune their rosters of drivers, scientists and engineers between each season -- perhaps poaching a pitman from a cross-country automotive adversary. You know, the sort of chicanery that's ripe for a "Drive To Survive" arc.

Metric-Distortion Bounds under Limited Information

Journal of Artificial Intelligence Research

In this work, we study the metric distortion problem in voting theory under a limited amount of ordinal information. Our primary contribution is threefold. First, we consider mechanisms that perform a sequence of pairwise comparisons between candidates. We show that a popular deterministic mechanism employed in many knockout phases yields distortion O(log m) while eliciting only m − 1 out of the Θ(m2 ) possible pairwise comparisons, where m represents the number of candidates. Our analysis for this mechanism leverages a powerful technical lemma developed by Kempe (AAAI ‘20). We also provide a matching lower bound on its distortion. In contrast, we prove that any mechanism which performs fewer than m−1 pairwise comparisons is destined to have unbounded distortion. Moreover, we study the power of deterministic mechanisms under incomplete rankings. Most notably, when agents provide their k-top preferences we show an upper bound of 6m/k + 1 on the distortion, for any k ∈ {1, 2, . . . , m}. Thus, we substantially improve over the previous bound of 12m/k established by Kempe (AAAI ‘20), and we come closer to matching the best-known lower bound. Finally, we are concerned with the sample complexity required to ensure near-optimal distortion with high probability. Our main contribution is to show that a random sample of Θ(m/ϵ2 ) voters suffices to guarantee distortion 3 + ϵ with high probability, for any sufficiently small ϵ > 0. This result is based on analyzing the sensitivity of the deterministic mechanism introduced by Gkatzelis, Halpern, and Shah (FOCS ‘20). Importantly, all of our sample-complexity bounds are distribution-independent. From an experimental standpoint, we present several empirical findings on real-life voting applications, comparing the scoring systems employed in practice with a mechanism explicitly minimizing (metric) distortion. Interestingly, for our case studies, we find that the winner in the actual competition is typically the candidate who minimizes the distortion.

Artificial Intelligence: How Formula One's McLaren team is using AI to fuel performance


'These bits of data shape a driver and can make him either a very good driver, a good driver, or a bad driver--it's just how you use it.'

How Formula One's McLaren team is using A.I. to fuel performance


Twin Omicron subvariants have taken over the U.S., but they're not quite identical. One is'the worst version of the virus we've seen'

Red Bull Racing suspends Juri Vips for use of racial slur on Twitch

Washington Post - Technology News

Athletes are increasingly spending their free time on Twitch, streaming to thousands of fans as they play games. Among NBA players, De'Aaron Fox of the Scramento Kings and Josh Hart of the New Orleans Pelicans both have their own Twitch channels. Lando Norris, a Formula One driver for McLaren Racing, has more than 1.3 million followers on Twitch.

Round 6 F1 GFT AI Driver Rankings: Verstappen Wins in Spain, Now leads Leclerc


It looked like another battle was shaping up Sunday in Barcelona with Charles Leclerc on Pole and leading early with Max Verstappen P2 and chasing hard. Then on Lap 27 everything changed as Leclerc's Ferrari lost power and he was forced to retire with a DNF in 20th place. Verstappen went on to win with teammate Sergio Perez finishing P2 and earning the Fastest Lap point. The win vaults Verstappen to the F1 Drivers Points lead and to the top of our F1 GFT AI Driver Rankings for Round 6. How do the Go Full Throttle AI models work? Algorithms The Go Full Throttle AI Driver Rankings is a cloud based predictive analytics system that uses our proprietary algorithms utilizing artificial intelligence and machine learning technology to dynamically tune and improve accuracy over time.

How Formula 1 teams are using tech to find an advantage in a lower budget cap season


Campbell is a journalist for ZDNet, covering technology's impact across the gamut of government, law, and regulation. Charles Leclerc of Ferrari, Max Verstappen of Oracle Red Bull Racing, Sergio Perez of Oracle Red Bull Racing, and Lando Norris of McLaren wait on the grid prior to the F1 Grand Prix of Australia at Melbourne Grand Prix Circuit. In the latest Formula One (F1) season, racing teams have been slapped with a drop in the budget cap, from $145 million to $140 million per team. The dip in approved expenditure has meant individual teams must place more emphasis on cost efficiency and resource management than ever before. By that same token, the technology used by F1 racing teams also carries more importance than in previous seasons. Each team approaches this cost cap challenge differently, but here are some of the ways F1 racing teams are using technology to shave off dollars this season, from the factory all the way to the race track.