CISA director Chris Krebs had been running a "Rumor Control" website debunking election fraud claims for weeks until the president fired him.
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Tuesday, November 17, 2020
Lenovo Black Friday sale: Save on ThinkPad laptops and more - CNET
Save on ThinkPads, Yoga convertibles and affordable yet powerful Legion gaming laptops.
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12 best TV shows to binge-watch on Disney Plus - CNET
Searching for more great shows like The Mandalorian? Let's round up Disney's best gems.
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From Apple to Samsung: 5G phones available right now - CNET
Check out the latest class of phones that have super-fast 5G connectivity.
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The 17 best TV shows to binge-watch on Amazon Prime Video - CNET
Looking for a great show to watch tonight? Let's round up Amazon's best gems.
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Target Black Friday 2020 ad scans: Save on Apple Watch, games and more - CNET
Scan all the deals now -- with price matching through Dec. 25.
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Gamer born without a hand gets Metal Gear Solid Venom Snake bionic arm - CNET
Open Bionics teams with Konami to create a 3D-printed official Metal Gear Solid-themed prosthetic arm.
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New watch contains pieces of Stephen Hawking's desk - CNET
Pricey timepieces celebrate the late physicist and best-selling author of A Brief History of Time.
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The Crown season 4: All your questions answered and that ending explained - CNET
Jumping into Netflix's slice of royal history? Here's everything you need to know about Princess Diana, Margaret Thatcher and more.
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Best PC speakers for 2020 - CNET
Looking for a pair of speakers for your computer? Here are our picks for top Windows and Mac PC speakers at various prices.
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5G phones in 2020: iPhone 12, Galaxy Note 20, Pixel 5 and more - CNET
Here are the top 5G phones that connect to the next-gen network.
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The Queen's Gambit: That ending explained and all your questions answered - CNET
Is the Netflix show based on a true story? Let's go through all those key details and more.
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The Queen's Gambit could be a game changer for women's chess - CNET
International Master Irina Berezina hopes the Netflix show inspires a whole new generation of female chess players.
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Tweets making fun of Twitter's new Fleets will not vanish in 24 hours - CNET
Tweets about Fleets are bringing the... heats? (Also, they're amusing.)
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2021 Mercedes-AMG GT Black Series is your new king of the Nürburgring - Roadshow
The production car lap time record fell once again on Tuesday with the Merc's stellar showing at the famed German circuit.
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What's Section 230? Everything you need to know about free speech on social media - CNET
Republicans and Democrats are considering reforms to weaken liability protections for social media companies.
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Google adds more COVID-19 details to its Maps app as cases surge - CNET
The service will include information on all detected cases in an area, local guidelines and testing sites.
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Twitter CEO Jack Dorsey to urge lawmakers to build on key internet law - CNET
Dorsey is scheduled to appear alongside Facebook CEO Mark Zuckerberg in a Senate hearing about the US election.
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The best battery-powered home security cameras to buy this year - CNET
Need a security camera that can go pretty much anywhere? Here are your best bets.
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Monday, November 16, 2020
Huawei is selling off Honor phone business to 'ensure its own survival' - CNET
The sale comes as crippling US sanctions take a heavy toll on the Chinese telecom giant.
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The Crown season 4: What it gets right (and wrong) about Princess Diana - CNET
Note: The Spencer tiara should NOT look like a Burger King crown.
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The Crown season 4: What it gets right (and wrong) about Margaret Thatcher - CNET
The Netflix drama introduces Britain's controversial first woman prime minister, played by Gillian Anderson.
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UFC 255: Figueiredo v Perez -- Start time, how to watch online and full fight card - CNET
It's hardly the most star-studded card, but UFC 255 is still worth a watch.
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2021 Jeep Wrangler Rubicon 392 is a V8 victory - Roadshow
After years of rumors, Jeep will finally produce a V8-powered Wrangler.
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Facebook labels reportedly ineffective at confining Trump's false election claims - CNET
The president claimed without supporting evidence that he was "up big" in the vote count and that his political opponents were "trying to steal the election."
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The Crown season 4 ending explained, and all your questions answered - CNET
Jumping into Netflix's slice of royal history? Here's everything you need to know about Princess Diana, Margaret Thatcher and more.
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Black Friday deals on smart home devices: $199 Shark Robot Vacuum available now, $19 Nest Mini coming next week - CNET
Get some great deals right now -- and check out what's coming soon.
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Best gifts for moms in 2020 - CNET
A collection of gifts carefully curated for these strange days.
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Best cash-back credit cards for November 2020 - CNET
Earn more rewards and credit every time you spend.
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Black Friday 2020 TV deals: LG and Sony OLED TVs on sale, $398 Samsung 58-inch available now, $478 Vizio 70-inch coming soon - CNET
It's the best time of the year to save on new TV.
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Best cases for iPhone 12 and iPhone 12 Pro - CNET
Here are some tasty case options for the iPhone 12, iPhone 12 Pro, iPhone 12 Max and iPhone 12 Mini.
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Barack Obama shares a playlist of favorite songs from White House years - CNET
Beyoncé, Bob Dylan, U2 and Eminem all made the former president's list.
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The best face masks for running outside - CNET
The lightweight, moisture-wicking face masks you need for all your runs.
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The Crown: Here's how to spot that real mouse darting through a scene - CNET
Say cheese: An unexpected rodent cameo has Netflix viewers squeaking.
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Airbnb files for IPO, shows it can actually make a profit - CNET
Still, the coronavirus pandemic has given a major blow to the short-term rental business.
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Xbox Game Pass: 18 awesome Xbox and PC games to play right now - CNET
If you own an Xbox -- Series X|S or One -- you should own a Game Pass subscription.
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MagSafe on iPhone 12: I still want USB-C, but I was wrong about Apple's magnetic charger - CNET
One day in and MagSafe has snapped itself into my life. But how many variations will there be?
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Sunday, November 15, 2020
Pininfarina Battista is smarter than your phone when roaming - Roadshow
The electric hypercar can automatically connect cell networks in over 50 countries based on location and signal strength.
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The best advent calendars that include food and drinks for 2020 - CNET
From wine and cheese to beer and pork rinds, these go way beyond chocolate.
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Newegg Black Friday deals: Save huge on an 85-inch Samsung TV, gaming PCs, laptops and more - CNET
The Newegg sale continues with price guarantees on everything from laptops to TVs to accessories.
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SpaceX, NASA Crew-1 mission makes historic launch to the ISS - CNET
Three NASA astronauts and a member of the Japanese Space Agency, JAXA, are on their way to the International Space Station
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Best student credit cards for November 2020 - CNET
The best cards for first-timers and students with a limited credit history.
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Best stocking stuffer ideas: Gifts under $25 - CNET
Keep your family and your wallet happy this holiday season by picking up these affordable gifts.
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Best Black Friday 2020 laptop deals: Huge Savings on HP, Lenovo, Microsoft Surface and more - CNET
You don't need to wait until Black Friday to get a deal on a new laptop.
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Amazon Echo early Black Friday deals: $75 Show 5, $13 Echo Flex and more - CNET
Why wait around for Nov. 27 when you can get Black Friday pricing today?
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The 30 best iPad games you need to play - CNET
There are some fantastic games on the iPad, here are our favourites.
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PS5 launch games: All the PlayStation 5 titles you can buy now - CNET
The PS5 has more than Demon's Souls and Miles Morales.
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Home Depot Black Friday sale is on now: See all the best deals on refrigerators, drills, and more - CNET
Save $650 on an LG smart refrigerator, $100 on a Ryobi six-piece cordless tool combo, and much more.
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Avatar 2: Kate Winslet is 'very proud' of breaking Tom Cruise's underwater record - CNET
Beating Cruise's record from Mission: Impossible Rogue Nation, Winslet held her breath underwater for seven minutes while filming an Avatar 2 water scene.
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The Queen's Gambit: That ending explained and all your questions answered - CNET
Is the Netflix show based on a true story? Let's go through all those key details and more.
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Best Black Friday AirPods deals: Airpods Pro at $200, a $49 savings - CNET
Apple headphones are now selling at or near all-time lows.
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8 PS5 UI tips and tricks to get the most out of your new PlayStation - CNET
Power settings, sub menus, this thing even has spoiler settings!
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Best Buy Black Friday 2020 ad: Huge sales on TVs, Speakers, Chromebooks, and more all month long - CNET
Looking for Best Buy's Black Friday ad circular? We have a scan of the whole ad, plus the best ads to keep an eye on.
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17 of the best TV shows to stream on Amazon Prime Video - CNET
Searching for a great show to watch tonight? Let's round up Amazon's best gems.
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PS5 vs Xbox Series X: The consoles we're buying and in which order - CNET
We asked CNET staff: Which console are you buying?
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The Crown season 4 ending explained, and all your questions answered - CNET
Jumping into Netflix's slice of royal history? Here's everything you need to know about Princess Diana, Margaret Thatcher and more.
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The 12 best TV shows to binge-watch on Disney Plus - CNET
Looking for more great shows like The Mandalorian? Let's round up Disney's best gems.
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The 32 best movies to stream on Disney Plus - CNET
Searching for entertainment other than Marvel and Star Wars? Let's round up the best gems on Disney Plus.
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31 of the best TV shows to see on Hulu - CNET
Searching for a great show to watch tonight? Here are some of the best Hulu has to offer.
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OnePlus 9 may sport a bigger screen, triple camera setup - CNET
New flagship handsets could debut in mid-March, 91Mobiles reports.
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Best robot vacuums for 2020: iRobot Roomba, Eufy, Electrolux, Neato and more - CNET
To pick our favorite robot vacuums we rigorously tested many leading models from iRobot, Electrolux, Eufy, Ecovacs, Neato and others.
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Whole Foods adds a new plant-based bacon - CNET
Hooray bacon sets a new high-water mark for bacon without the pig.
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NFL 2020: How to watch Seahawks vs. Rams, Bills vs. Cardinals, RedZone and the rest of Week 10 today without cable - CNET
You don't need cable to watch the Week 10 action.
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The incredible Pocketalk Classic mobile translator is on sale for $99 - CNET
Lowest price ever. It's like something out of Star Trek, and it's bundled with two free years of global data.
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Epic's free game this week is a 'bullet hell' action game with a lot of typing - CNET
The Textorcist: The Story of Ray Bibbia is the most unusual indie game you'll play all year.
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Apple Watch Series 6 vs. Fitbit Sense: Top smartwatches go head to head - CNET
We compare the flagship Apple and Fitbit smartwatches to help you decide between the two.
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Apple HomePod Mini review: iPhone users will love this $99 Siri smart speaker - CNET
At $99, the HomePod Mini is Apple's most affordable smart home speaker. It's worth a look, especially if you're already invested in Apple's ecosystem.
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Apple's remaking Mac computers, and it's taking control to do it - CNET
Apple's new MacBook Air, MacBook Pro and Mac Mini look the same on the outside, but they're getting new tricks that PCs will struggle to emulate.
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OnePlus Nord N10 5G review: An affordable 5G phone with few compromises - CNET
Available in Europe first and then North America, the Nord N10 5G expands OnePlus' offering of affordable 5G phones.
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Best car plastic restorer: Chemical Guys, Meguiar's and more compared - Roadshow
Get your car's trim looking brand new with our favorite plastic restorers on the market.
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Facebook, Twitter CEOs to testify before the Senate: How to watch Tuesday - CNET
Mark Zuckerberg and Jack Dorsey will appear before a Senate committee on Tuesday, Nov. 17, 2020 at 10:00 a.m. ET/7:00 a.m. PT.
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MacOS Big Sur: Apple's new M1 chip will make apps run faster and smoother - CNET
You'll be able to work seamlessly between your iPhone, iPad and Mac with new universal apps for MacOS.
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Pixel 5 specs vs. iPhone 11, Galaxy S20 FE and OnePlus 8 - CNET
Check out how Google's latest flagship stacks up against its competitors' latest releases.
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Best cheap gaming laptops under $1,000 for 2020 - CNET
Better entry-level graphics chips mean terrific performance for less money.
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Best laptop for 2020 - CNET
Here are our top-rated picks for all types of users, including creatives and gamers, and even our favorite cheap laptops and budget-friendly options.
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Google Pixel 5's wimpy camera is driving me to the iPhone 12 - CNET
Commentary: For this photo enthusiast, the Pixel 5's middling camera hardware has undercut Google's advanced image processing.
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iPhone 12 Mini review: Apple's smallest is a one-handed phone user's dream - CNET
Move over iPhone SE. This is the small iPhone people have been asking Apple to make.
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iPhone 12 vs. Pixel 5: Apple and Google's 5G flagships compared - CNET
For the operating system agnostic, the choice between Pixel 5 and iPhone 12 might come down to design, camera, battery, performance -- or price.
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Saturday, November 14, 2020
The best Prime Day 2020 deals still available: Get a MacBook Air for $850, a Roku for $27, AirPods Pro for $199 - CNET
The big event is over, but a handful of tasty bargains remain.
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Qualcomm gets OK to sell 4G chips to Huawei, despite US ban, report says - CNET
But it's unclear whether Qualcomm could get licenses to sell 5G chips to the China-based company, Reuters reports.
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The best gifts for girls ages 9-12 - CNET
These presents will delight preteen girls this holiday season.
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Friday, November 13, 2020
Affordable holiday gift guide for car lovers in 2020 - Roadshow
Here's a diverse range of holiday gift ideas for the gearheads in your life, and each one costs less than $100.
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Solv Health, which sells patient management software to health care providers, mainly urgent care clinics, raises $27M Series B+ led by Acrew Capital (Kia Kokalitcheva/Axios)
Solv Health, which sells patient management software to health care providers, mainly urgent care clinics, raises $27M Series B+ led by Acrew Capital — Solv Health, a startup that sells health care providers digital tools to manage patients, has raised $27 million in new funding led by Acrew Capital …
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Predicting qualification ranking based on practice session performance for Formula 1 Grand Prix
If you’re a Formula 1 (F1) fan, have you ever wondered why F1 teams have very different performances between qualifying and practice sessions? Why do they have multiple practice sessions in the first place? Can practice session results actually tell something about the upcoming qualifying race? In this post, we answer these questions and more. We show you how we can predict qualifying results based on practice session performances by harnessing the power of data and machine learning (ML). These predictions are being integrated into the new “Qualifying Pace” insight for each F1 Grand Prix (GP). This work is part of the continuous collaboration between F1 and the Amazon ML Solutions Lab to generate new F1 Insights powered by AWS.
Each F1 GP consists of several stages. The event starts with three practice sessions (P1, P2, and P3), followed by a qualifying (Q) session, and then the final race. Teams approach practice and qualifying sessions differently because these sessions serve different purposes. The practice sessions are the teams’ opportunities to test out strategies and tire compounds to gather critical data in preparation for the final race. They observe the car’s performance with different strategies and tire compounds, and use this to determine their overall race strategy.
In contrast, qualifying sessions determine the starting position of each driver on race day. Teams focus solely on obtaining the fastest lap time. Because of this shift in tactics, Friday and Saturday practice session results often fail to accurately predict the qualifying order.
In this post, we introduce deterministic and probabilistic methods to model the time difference between the fastest lap time in practice sessions and the qualifying session (∆t = tq-tp). The goal is to more accurately predict the upcoming qualifying standings based on the practice sessions.
Error sources of ∆t
The delta of the fastest lap time between practice and qualifying sessions (∆t) comes primarily from variations in fuel level and tire grip.
A higher fuel level adds weight to the car and reduces the speed of the car. For practice sessions, teams vary the fuel level as they please. For the second practice session (P2), it’s common to begin with a low fuel level and run with more fuel in the latter part of the session. During qualifying, teams use minimal fuel levels in order to record the fastest lap time. The impact of fuel on lap time varies from circuit to circuit, depending on how many straights the circuit has and how long these straights are.
Tires also play a significant role in an F1 car’s performance. During each GP event, the tire supplier brings various tire types with varying compounds suitable for different racing conditions. Two of these are for wet circuit conditions: intermediate tires for light standing water and wet tires for heavy standing water. The remaining dry running tires can be categorized into three compound types: hard, medium, and soft. These tire compounds provide different grips to the circuit surface. The more grip the tire provides, the faster the car can run.
Past racing results showed that car performance dropped significantly when wet tires were used. For example, in the 2018 Italy GP, because the P1 session was wet and the qualifying session was dry, the fastest lap time in P1 was more than 10 seconds slower than the qualifying session.
Among the dry running types, the hard tire provides the least grip but is the most durable, whereas the soft tire has the most grip but is the least durable. Tires degrade over the course of a race, which reduces the tire grip and slows down the car. Track temperature and moisture affects the progression of degradation, which in turn changes the tire grip. As in the case with fuel level, tire impact on lap time changes from circuit to circuit.
Data and attempted approaches
Given this understanding of factors that can impact lap time, we can use fuel level and tire grip data to estimate the final qualifying lap time based on known practice session performance. However, as of this writing, data records to directly infer fuel level and tire grip during the race are not available. Therefore, we take an alternative approach with data we can currently obtain.
The data we used in the modeling were records of fastest lap times for each GP since 1950 and partial years of weather data for the corresponding sessions. The lap times data included the fastest lap time for each session (P1, P2, P3, and Q) of each GP with the driver, car and team, and circuit name (publicly available on F1’s website). Track wetness and temperature for each corresponding session was available in the weather data.
We explored two implicit methods with the following model inputs: the team and driver name, and the circuit name. Method one was a rule-based empirical model that attributed observed to circuits and teams. We estimated the latent parameter values (fuel level and tire grip differences specific to each team and circuit) based on their known lap time sensitivities. These sensitivities were provided by F1 and calculated through simulation runs on each circuit track. Method two was a regression model with driver and circuit indicators. The regression model learned the sensitivity of ∆t for each driver on each circuit without explicitly knowing the fuel level and tire grip exerted. We developed and compared deterministic models using XGBoost and AutoGluon, and probabilistic models using PyMC3.
We built models using race data from 2014 to 2019, and tested against race data from 2020. We excluded data from before 2014 because there were significant car development and regulation changes over the years. We removed races in which either the practice or qualifying session was wet because ∆t for those sessions were considered outliers.
Managed model training with Amazon SageMaker
We trained our regression models on Amazon SageMaker.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. Specifically for model training, it provides many features to assist with the process.
For our use case, we explored multiple iterations on the choices of model feature sets and hyperparameters. Recording and comparing the model metrics of interest was critical to choosing the most suitable model. The Amazon SageMaker API allowed customized metrics definition prior to launching a model training job, and easy retrieval after the training job was complete. Using the automatic model tuning feature reduced the mean squared error (MSE) metric on the test data by 45% compared to the default hyperparameter choice.
We trained an XGBoost model using the Amazon SageMaker’s built-in implementation. Its built-in implementation allowed us to run model training through a general estimator interface. This approach provided better logging, superior hyperparameter validation, and a larger set of metrics than the original implementation.
Rule-based model
In the rule-based approach, we reason that the differences of lap times ∆t primarily come from systematic variations of tire grip for each circuit and fuel level for each team between practice and qualifying sessions. After accounting for these known variations, we assume residuals are random small numbers with a mean of zero. ∆t can be modeled with the following equation:
∆tf(c) and ∆tg(c) are known sensitivities of fuel mass and tire grip, and is the residual. A hierarchy exists among the factors contained in the equation. We assume grip variations for each circuit (g(c)) are at the top level. Under each circuit, there are variations of fuel level across teams (f(t,c)).
To further simplify the model, we neglect because we assume it is small. We further assume fuel variation for each team across all circuits is the same (i.e., f(t,c) = f(t)). We can simplify the model to the following:
Because ∆tf(c) and ∆tg(c) are known, f(t) and g(c), we can estimate team fuel variations and tire grip variations from the data.
The differences in the sensitivities depend on the characteristics of circuits. From the following track maps, we can observe that the Italian GP circuit has fewer corner turns and the straight sections are longer compared to the Singapore GP circuit. Additional tire grip gives a larger advantage in the Singapore GP circuit.
ML regression model
For the ML regression method, we don’t directly model the relation between and fuel level and grip variations. Instead, we fit the following regression model with just the circuit, team, and driver indicator variables:
Ic, It, and Id represent the indicator variables for circuits, teams, and drivers.
Hierarchical Bayesian model
Another challenge with modeling the race pace was due to noisy measurements in lap times. The magnitude of random effect (ϵ) of ∆t could be non-negligible. Such randomness might come from drivers’ accidental drift from their normal practice at the turns or random variations of drivers’ efforts during practice sessions. With deterministic approaches, such random effect wasn’t appropriately captured. Ideally, we wanted a model that could quantify uncertainty about the predictions. Therefore, we explored Bayesian sampling methods.
With a hierarchical Bayesian model, we account for the hierarchical structure of the error sources. As with the rule-based model, we assume grip variations for each circuit (g(c))) are at the top level. The additional benefit of a hierarchical Bayesian model is that it incorporates individual-level variations when estimating group-level coefficients. It’s a middle ground between two extreme views of data. One extreme is to pool data for every group (circuit and driver) without considering the intrinsic variations among groups. The other extreme is to train a regression model for each circuit or driver. With 21 circuits, this amounts to 21 regression models. With a hierarchical model, we have a single model that considers the variations simultaneously at the group and individual level.
We can mathematically describe the underlying statistical model for the hierarchical Bayesian approach as the following varying intercepts model:
Here, i represents the index of each data observation, j represents the index of each driver, and k represents the index of each circuit. μjk represents the varying intercept for each driver under each circuit, and θk represents the varying intercept for each circuit. wp and wq represent the wetness level of the track during practice and qualifying sessions, and ∆T represents the track temperature difference.
Test models in the 2020 races
After predicting ∆t, we added it into the practice lap times to generate predictions of qualifying lap times. We determined the final ranking based on the predicted qualifying lap times. Finally, we compared predicted lap times and rankings with the actual results.
The following figure compares the predicted rankings and the actual rankings for all three practice sessions for the Austria, Hungary, and Great Britain GPs in 2020 (we exclude P2 for the Hungary GP because the session was wet).
For the Bayesian model, we generated predictions with an uncertainty range based on the posterior samples. This enabled us to predict the ranking of the drivers relatively with the median while accounting for unexpected outcomes in the drivers’ performances.
The following figure shows an example of predicted qualifying lap times (in seconds) with an uncertainty range for selected drivers at the Austria GP. If two drivers’ prediction profiles are very close (such as MAG and GIO), it’s not surprising that either driver might be the faster one in the upcoming qualifying session.
Metrics on model performance
To compare the models, we used mean squared error (MSE) and mean absolute error (MAE) for lap time errors. For ranking errors, we used rank discounted cumulative gain (RDCG). Because only the top 10 drivers gain points during a race, we used RDCG to apply more weight to errors in the higher rankings. For the Bayesian model output, we used median posterior value to generate the metrics.
The following table shows the resulting metrics of each modeling approach for the test P2 and P3 sessions. The best model by each metric for each session is highlighted.
| MODEL | MSE | MAE | RDCG | |||
| P2 | P3 | P2 | P3 | P2 | P3 | |
| Practice raw | 2.822 | 1.053 | 1.544 | 0.949 | 0.92 | 0.95 |
| Rule-based | 0.349 | 0.186 | 0.462 | 0.346 | 0.88 | 0.95 |
| XGBoost | 0.358 | 0.141 | 0.472 | 0.297 | 0.91 | 0.95 |
| AutoGluon | 0.567 | 0.351 | 0.591 | 0.459 | 0.90 | 0.96 |
| Hierarchical Bayesian | 0.431 | 0.186 | 0.521 | 0.332 | 0.87 | 0.92 |
All models reduced the qualifying lap time prediction errors significantly compared to directly using the practice session results. Using practice lap times directly without considering pace correction, the MSE on the predicted qualifying lap time was up to 2.8 seconds. With machine learning methods which automatically learned pace variation patterns for teams and drivers on different circuits, we brought the MSE down to smaller than half a second. The resulting prediction was a more accurate representation of the pace in the qualifying session. In addition, the models improved the prediction of rankings by a small margin. However, there was no one single approach that outperformed all others. This observation highlighted the effect of random errors on the underlying data.
Summary
In this post, we described a new Insight developed by the Amazon ML Solutions Lab in collaboration with Formula 1 (F1).
This work is part of the six new F1 Insights powered by AWS that are being released in 2020, as F1 continues to use AWS for advanced data processing and ML modeling. Fans can expect to see this new Insight unveiled at the 2020 Turkish GP to provide predictions for the upcoming qualifying races at practice sessions.
If you’d like help accelerating the use of ML in your products and services, please contact the Amazon ML Solutions Lab .
About the Author
Guang Yang is a data scientist at the Amazon ML Solutions Lab where he works with customers across various verticals and applies creative problem solving to generate value for customers with state-of-the-art ML/AI solutions.
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Predicting qualification ranking based on practice session performance for Formula 1 Grand Prix
If you’re a Formula 1 (F1) fan, have you ever wondered why F1 teams have very different performances between qualifying and practice sessions? Why do they have multiple practice sessions in the first place? Can practice session results actually tell something about the upcoming qualifying race? In this post, we answer these questions and more. We show you how we can predict qualifying results based on practice session performances by harnessing the power of data and machine learning (ML). These predictions are being integrated into the new “Qualifying Pace” insight for each F1 Grand Prix (GP). This work is part of the continuous collaboration between F1 and the Amazon ML Solutions Lab to generate new F1 Insights powered by AWS.
Each F1 GP consists of several stages. The event starts with three practice sessions (P1, P2, and P3), followed by a qualifying (Q) session, and then the final race. Teams approach practice and qualifying sessions differently because these sessions serve different purposes. The practice sessions are the teams’ opportunities to test out strategies and tire compounds to gather critical data in preparation for the final race. They observe the car’s performance with different strategies and tire compounds, and use this to determine their overall race strategy.
In contrast, qualifying sessions determine the starting position of each driver on race day. Teams focus solely on obtaining the fastest lap time. Because of this shift in tactics, Friday and Saturday practice session results often fail to accurately predict the qualifying order.
In this post, we introduce deterministic and probabilistic methods to model the time difference between the fastest lap time in practice sessions and the qualifying session (∆t = tq-tp). The goal is to more accurately predict the upcoming qualifying standings based on the practice sessions.
Error sources of ∆t
The delta of the fastest lap time between practice and qualifying sessions (∆t) comes primarily from variations in fuel level and tire grip.
A higher fuel level adds weight to the car and reduces the speed of the car. For practice sessions, teams vary the fuel level as they please. For the second practice session (P2), it’s common to begin with a low fuel level and run with more fuel in the latter part of the session. During qualifying, teams use minimal fuel levels in order to record the fastest lap time. The impact of fuel on lap time varies from circuit to circuit, depending on how many straights the circuit has and how long these straights are.
Tires also play a significant role in an F1 car’s performance. During each GP event, the tire supplier brings various tire types with varying compounds suitable for different racing conditions. Two of these are for wet circuit conditions: intermediate tires for light standing water and wet tires for heavy standing water. The remaining dry running tires can be categorized into three compound types: hard, medium, and soft. These tire compounds provide different grips to the circuit surface. The more grip the tire provides, the faster the car can run.
Past racing results showed that car performance dropped significantly when wet tires were used. For example, in the 2018 Italy GP, because the P1 session was wet and the qualifying session was dry, the fastest lap time in P1 was more than 10 seconds slower than the qualifying session.
Among the dry running types, the hard tire provides the least grip but is the most durable, whereas the soft tire has the most grip but is the least durable. Tires degrade over the course of a race, which reduces the tire grip and slows down the car. Track temperature and moisture affects the progression of degradation, which in turn changes the tire grip. As in the case with fuel level, tire impact on lap time changes from circuit to circuit.
Data and attempted approaches
Given this understanding of factors that can impact lap time, we can use fuel level and tire grip data to estimate the final qualifying lap time based on known practice session performance. However, as of this writing, data records to directly infer fuel level and tire grip during the race are not available. Therefore, we take an alternative approach with data we can currently obtain.
The data we used in the modeling were records of fastest lap times for each GP since 1950 and partial years of weather data for the corresponding sessions. The lap times data included the fastest lap time for each session (P1, P2, P3, and Q) of each GP with the driver, car and team, and circuit name (publicly available on F1’s website). Track wetness and temperature for each corresponding session was available in the weather data.
We explored two implicit methods with the following model inputs: the team and driver name, and the circuit name. Method one was a rule-based empirical model that attributed observed to circuits and teams. We estimated the latent parameter values (fuel level and tire grip differences specific to each team and circuit) based on their known lap time sensitivities. These sensitivities were provided by F1 and calculated through simulation runs on each circuit track. Method two was a regression model with driver and circuit indicators. The regression model learned the sensitivity of ∆t for each driver on each circuit without explicitly knowing the fuel level and tire grip exerted. We developed and compared deterministic models using XGBoost and AutoGluon, and probabilistic models using PyMC3.
We built models using race data from 2014 to 2019, and tested against race data from 2020. We excluded data from before 2014 because there were significant car development and regulation changes over the years. We removed races in which either the practice or qualifying session was wet because ∆t for those sessions were considered outliers.
Managed model training with Amazon SageMaker
We trained our regression models on Amazon SageMaker.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. Specifically for model training, it provides many features to assist with the process.
For our use case, we explored multiple iterations on the choices of model feature sets and hyperparameters. Recording and comparing the model metrics of interest was critical to choosing the most suitable model. The Amazon SageMaker API allowed customized metrics definition prior to launching a model training job, and easy retrieval after the training job was complete. Using the automatic model tuning feature reduced the mean squared error (MSE) metric on the test data by 45% compared to the default hyperparameter choice.
We trained an XGBoost model using the Amazon SageMaker’s built-in implementation. Its built-in implementation allowed us to run model training through a general estimator interface. This approach provided better logging, superior hyperparameter validation, and a larger set of metrics than the original implementation.
Rule-based model
In the rule-based approach, we reason that the differences of lap times ∆t primarily come from systematic variations of tire grip for each circuit and fuel level for each team between practice and qualifying sessions. After accounting for these known variations, we assume residuals are random small numbers with a mean of zero. ∆t can be modeled with the following equation:
∆tf(c) and ∆tg(c) are known sensitivities of fuel mass and tire grip, and is the residual. A hierarchy exists among the factors contained in the equation. We assume grip variations for each circuit (g(c)) are at the top level. Under each circuit, there are variations of fuel level across teams (f(t,c)).
To further simplify the model, we neglect because we assume it is small. We further assume fuel variation for each team across all circuits is the same (i.e., f(t,c) = f(t)). We can simplify the model to the following:
Because ∆tf(c) and ∆tg(c) are known, f(t) and g(c), we can estimate team fuel variations and tire grip variations from the data.
The differences in the sensitivities depend on the characteristics of circuits. From the following track maps, we can observe that the Italian GP circuit has fewer corner turns and the straight sections are longer compared to the Singapore GP circuit. Additional tire grip gives a larger advantage in the Singapore GP circuit.
ML regression model
For the ML regression method, we don’t directly model the relation between and fuel level and grip variations. Instead, we fit the following regression model with just the circuit, team, and driver indicator variables:
Ic, It, and Id represent the indicator variables for circuits, teams, and drivers.
Hierarchical Bayesian model
Another challenge with modeling the race pace was due to noisy measurements in lap times. The magnitude of random effect (ϵ) of ∆t could be non-negligible. Such randomness might come from drivers’ accidental drift from their normal practice at the turns or random variations of drivers’ efforts during practice sessions. With deterministic approaches, such random effect wasn’t appropriately captured. Ideally, we wanted a model that could quantify uncertainty about the predictions. Therefore, we explored Bayesian sampling methods.
With a hierarchical Bayesian model, we account for the hierarchical structure of the error sources. As with the rule-based model, we assume grip variations for each circuit (g(c))) are at the top level. The additional benefit of a hierarchical Bayesian model is that it incorporates individual-level variations when estimating group-level coefficients. It’s a middle ground between two extreme views of data. One extreme is to pool data for every group (circuit and driver) without considering the intrinsic variations among groups. The other extreme is to train a regression model for each circuit or driver. With 21 circuits, this amounts to 21 regression models. With a hierarchical model, we have a single model that considers the variations simultaneously at the group and individual level.
We can mathematically describe the underlying statistical model for the hierarchical Bayesian approach as the following varying intercepts model:
Here, i represents the index of each data observation, j represents the index of each driver, and k represents the index of each circuit. μjk represents the varying intercept for each driver under each circuit, and θk represents the varying intercept for each circuit. wp and wq represent the wetness level of the track during practice and qualifying sessions, and ∆T represents the track temperature difference.
Test models in the 2020 races
After predicting ∆t, we added it into the practice lap times to generate predictions of qualifying lap times. We determined the final ranking based on the predicted qualifying lap times. Finally, we compared predicted lap times and rankings with the actual results.
The following figure compares the predicted rankings and the actual rankings for all three practice sessions for the Austria, Hungary, and Great Britain GPs in 2020 (we exclude P2 for the Hungary GP because the session was wet).
For the Bayesian model, we generated predictions with an uncertainty range based on the posterior samples. This enabled us to predict the ranking of the drivers relatively with the median while accounting for unexpected outcomes in the drivers’ performances.
The following figure shows an example of predicted qualifying lap times (in seconds) with an uncertainty range for selected drivers at the Austria GP. If two drivers’ prediction profiles are very close (such as MAG and GIO), it’s not surprising that either driver might be the faster one in the upcoming qualifying session.
Metrics on model performance
To compare the models, we used mean squared error (MSE) and mean absolute error (MAE) for lap time errors. For ranking errors, we used rank discounted cumulative gain (RDCG). Because only the top 10 drivers gain points during a race, we used RDCG to apply more weight to errors in the higher rankings. For the Bayesian model output, we used median posterior value to generate the metrics.
The following table shows the resulting metrics of each modeling approach for the test P2 and P3 sessions. The best model by each metric for each session is highlighted.
| MODEL | MSE | MAE | RDCG | |||
| P2 | P3 | P2 | P3 | P2 | P3 | |
| Practice raw | 2.822 | 1.053 | 1.544 | 0.949 | 0.92 | 0.95 |
| Rule-based | 0.349 | 0.186 | 0.462 | 0.346 | 0.88 | 0.95 |
| XGBoost | 0.358 | 0.141 | 0.472 | 0.297 | 0.91 | 0.95 |
| AutoGluon | 0.567 | 0.351 | 0.591 | 0.459 | 0.90 | 0.96 |
| Hierarchical Bayesian | 0.431 | 0.186 | 0.521 | 0.332 | 0.87 | 0.92 |
All models reduced the qualifying lap time prediction errors significantly compared to directly using the practice session results. Using practice lap times directly without considering pace correction, the MSE on the predicted qualifying lap time was up to 2.8 seconds. With machine learning methods which automatically learned pace variation patterns for teams and drivers on different circuits, we brought the MSE down to smaller than half a second. The resulting prediction was a more accurate representation of the pace in the qualifying session. In addition, the models improved the prediction of rankings by a small margin. However, there was no one single approach that outperformed all others. This observation highlighted the effect of random errors on the underlying data.
Summary
In this post, we described a new Insight developed by the Amazon ML Solutions Lab in collaboration with Formula 1 (F1).
This work is part of the six new F1 Insights powered by AWS that are being released in 2020, as F1 continues to use AWS for advanced data processing and ML modeling. Fans can expect to see this new Insight unveiled at the 2020 Turkish GP to provide predictions for the upcoming qualifying races at practice sessions.
If you’d like help accelerating the use of ML in your products and services, please contact the Amazon ML Solutions Lab .
About the Author
Guang Yang is a data scientist at the Amazon ML Solutions Lab where he works with customers across various verticals and applies creative problem solving to generate value for customers with state-of-the-art ML/AI solutions.
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Hisense 70-inch 4K UHD Smart Android TV deal is still available for $400 - CNET
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What The Crown season 4 gets right (and wrong) about Princess Diana - CNET
Real vs Netflix reel: The Spencer tiara should NOT look like a Burger King crown.
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Apple Watch SE Black Friday deal: 44mm back to $260 at Amazon - CNET
The weekend sale on the Apple Watch SE returns after a brief hiatus.
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AI services company C3.ai files for IPO, reports revenue of $157M in the fiscal year ending April 2020, up 71% YoY, and a deficit of $293M at the end of July (Tiernan Ray/ZDNet)
AI services company C3.ai files for IPO, reports revenue of $157M in the fiscal year ending April 2020, up 71% YoY, and a deficit of $293M at the end of July — The software-as-a-service company that has been using masses of GPUs to run deep learning programs plans to list under the ticker “AI.”
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Ticketmaster's UK wing fined ~$1.6M by the UK ICO after a report found they failed to put appropriate security measures in place prior to their 2018 data breach (Shoshana Wodinsky/Gizmodo)
Ticketmaster's UK wing fined ~$1.6M by the UK ICO after a report found they failed to put appropriate security measures in place prior to their 2018 data breach — Ticketmaster's UK wing has been fined £1.25 million pounds (roughly $1.6 millions) following an investigation …
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AI Weekly: Tech, power, and building the Biden administration
The presidential election is over, and debate over tech, power, and what the Biden administration should look like is in full swing.Read More
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Hackers sponsored by Russia and North Korea are targeting COVID-19 researchers
Enlarge (credit: Getty Images)
Hackers sponsored by the Russian and North Korean governments have been targeting companies directly involved in researching vaccines and treatments for COVID-19, and in some cases, the attacks have succeeded, Microsoft said on Friday.
In all, there are seven prominent companies that have been targeted, Microsoft Corporate VP for Customer Security & Trust Tom Burt said. They include vaccine makers with COVID-19 vaccines in various clinical trial stages, a clinical research organization involved in trials, and a developer of a COVID-19 test. Also targeted were organizations with contracts with or investments from governmental agencies around the world for COVID-19-related work. The targets are located in the US, Canada, France, India, and South Korea.
“Microsoft is calling on the world’s leaders to affirm that international law protects health care facilities and to take action to enforce the law,” Burt wrote in a blog post. “We believe the law should be enforced not just when attacks originate from government agencies but also when they originate from criminal groups that governments enable to operate—or even facilitate—within their borders. This is criminal activity that cannot be tolerated.”
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