Indianapolis 500 practice, top speeds, crashes at Indianapolis Motor Speedway today 5/16/25
The 109th Indianapolis 500 is scheduled for Sunday, May 25, 2025, on the 2.5-mile Indianapolis Motor Speedway oval. The track action promises to heat up as Fast Friday brings more speed and more tension.
Cars receive a turbocharger boost to replicate qualifying setups on Saturday and Sunday, May 17-18. Speeds will rise from previous practice days, which are run with lower boost replicating race setups.
Advertisement
There was an interesting mix of teams atop the Thursday's leaderboard: Josef Newgarden (Penske), 226.632 mph; Scott Dixon (Ganassi), 225.457; Conor Daly (Juncos Hollinger), 224.894; Pato O'Ward (Arrow McLaren), 224.467; Ryan Hunter-Reay (Dreyer & Reinbold/Cusick), 223.983. Newgarden also has the best no-tow lap at 222.555.
IMS officials announced Friday morning that the local television live blackout has been lifted in anticipation of all reserved seats being sold by early next week.
It's a lot: What's happening at Indianapolis Motor Speedway?
Nathan Brown is your best Indy 500 follow, and sign up for IndyStar's motorsports newsletter. We will have speed updates and highlights all afternoon, so remember to refresh.
Indy 500 practice weather forecast today, May 16
A 50% chance of rain with high temperatures in the low 80s.
Indy 500 practice results on Thursday, May 15
Indy 500 practice, qualifying, race tickets
Practice tickets for May 15-16 and 19 are $20. Buy tickets here
Qualifying tickets for May 17-18 are $30. Buy tickets here
Carb Day practice tickets for May 23 are $49; practice and concert tickets are $88. Buy tickets here
Legends Day tickets for May 24 are $20. Buy tickets here
2025 Indianapolis 500 general admission tickets for May 25 start at $55, reserved seats at $80. Buy tickets here
Advertisement
Find Indy 500 tickets on StubHub
What channel is Indy 500 practice, qualifying and the race on?
TV: Coverage will be on Fox, FS1 or FS2, depending on day and time. Will Buxton is the play-by-play voice, with analysts James Hinchcliffe and Townsend Bell.
How can I stream the Indy 500 practice, qualifying and race?
FoxSports.com, Fox Sports app.
How can I listen to Indy 500 practice, qualifying and the race?
IndyCar Nation is on SiriusXM Channel 218, IndyCar Live and the IndyCar Radio Network (check affiliates for each race)
Indy 500 drivers for 2025
(Team and drivers; *-Indianapolis 500 only)
Advertisement
This article originally appeared on Indianapolis Star: Indy 500 Fast Friday practice results, crashes, top lap speeds today, no tow
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Fox Sports
10 hours ago
- Fox Sports
2025 Indianapolis Colts Preseason Schedule: Dates, Times, TV Channels
National Football League 2025 Indianapolis Colts Preseason Schedule: Dates, Times, TV Channels Published Aug. 4, 2025 3:42 p.m. ET share facebook x reddit link The Indianapolis Colts enter the 2025 NFL preseason looking to rebound after finishing 8–9 in 2024, placing second in the AFC South but missing the playoffs for the fourth straight year. As Indianapolis aims to return to postseason contention in 2025, here's a complete look at the Colts' 2025 preseason schedule, including dates, times and TV channels. Indianapolis Colts 2025 Preseason Schedule Check out the team's preseason schedule below: 'I absolutely own the Colts' - Rob Gronkowski on feeling at home at Indy 500 | INDYCAR on FOX How will the Colts do this season? Ben Arthur predicts that the Colts will finish the year at 8-9. Here's Arthur: "A revamped secondary under new defensive coordinator Lou Anarumo will give the Colts a leg up against the quarterbacks on their schedule. But without a consistent, reliable passer of its own, Indianapolis could struggle in tight division matchups and down the stretch of 2025. That leads the Colts to their third finish under .500 in four years." share Get more from the National Football League Follow your favorites to get information about games, news and more
Epoch Times
10 hours ago
- Epoch Times
The First Indianapolis 500 and the Man Who Invented the Rearview Mirror
The inaugural Indianapolis 500-Mile Race was unprecedented. Some 90,000 spectators packed the Indianapolis Motor Speedway on May 30, 1911, to catch a glimpse of a high-speed, groundbreaking, dangerous, and hair-raising event. Ray Harroun pushed the margins of innovation, which made him stand out among the 40 drivers. Epitomizing both the progress and potential of racing, he rolled onto the brick-paved oval in something he had designed himself. It was bright yellow and black, emblazoned with the number 32, and nicknamed the Marmon Wasp as a nod to its color combination and manufacturer.
Forbes
11 hours ago
- Forbes
Indy 500 & Robotic Cars Race On Challenging Laguna Seca Raceway
IAC (Indy Autonomous Challenge) robotic race cars performed at the historic WeatherTech Raceway Laguna Seca in Monterey, CA on July 24, 2025. Eight teams participated in individual time trials across a challenging track (~2.25 miles/lap) with 11 sharp turns (including the famous corkscrew, see Figure 1 below), and steep elevation changes (180 feet). This is a first for robotic racing in the history of the WeatherTech Raceway. It is also the first time that IAC has performed during a week of Indy 500 racing events, culminating on 27 July 2025 with the Java House Grand Prix race. IAC has been hosting robotic car races since 2021 at tracks like the Indiana Motor Speedway (home of the Indy 500) and the Las Vegas Motor Speedway. The event in Las Vegas, held in concert with the Consumer Electronics Show (CES) in January 2025, featured a 4-car race, something not attempted before. At the recent event in Monterey (the first time an IAC event was held during the same time period and track as the Indy 500 race), the competition featured single race cars at a time. Given the difficulty of the track and first-time participation by the different teams, multi-car racing was considered to be too risky. The course is particularly challenging for robotic cars given the perception and localization challenges due to the sharp turns and elevation changes. Stable vehicle control at high speeds is also difficult because of these factors. IAC event competitors include university teams (typically with Ph.D. level students and faculty advisors) from the USA, Germany, Italy and Korea. Participating teams for this event included: IAC races are designed to engage top robotics, artificial intelligence (AI) and vehicle dynamics/control talent across universities. The goal is to nurture practical experience in physical AI, and use the intellectual property to understand autonomy in high speed, commercial applications like autonomous cars and drones. The hardware platforms are identical (car, engines, tires, sensors, compute). Teams compete based on the quality of the AI and robotic control at high speeds, and low latency perception and decision making, PolyMOVE-MSU won the event with a winning lap time of ~90 seconds over the 2.25-mile course (average speed of ~90 mph). The peak speed reached was 148 mph. This is the first ever experience for an autonomous racing competition on a road-course circuit in the USA (Figure 2). The Purdue team was a strong competitor and came in second, with KAIST in third place. A couple of cars (CAST-Caltech and Tiger Racing) were unable to negotiate the difficult corkscrew turn and had to be rescued by tow trucks (human driven! We are yet to get to autonomous tow trucks !). According to Paul Mitchell, CEO of Indy Autonomous Challenge and its parent company Aidoptation: 'Our university research teams stepped up to this challenge, advancing the field of AI and autonomy by pushing vehicle dynamics to the absolute edge and laying down lap times that only the best human drivers can achieve". Professor Sergio Savaresi (Polytecnico Di Milano) and Rodrigo Senofieni (former Ph.D. student of Professor Savaresi, and currently at Aidoptation) are the technical leads for the PoliMOVE-MSU team. Per Professor Savaresi, the key enablers for their winning performance were: Professor Savaresi commented: 'Our team spent a lot of time in simulation to perfect the AI driver's decision-making capabilities. I am incredibly proud of this team". Purdue entered the IAC in 2021, but decided to reorganize 18 months ago to grow capabilities and focus. IAC provided guidance and sharing of best practices, and Purdue's Dean of Engineering, Arvind Raman internally championed the initiative. Dan Williams, an ex-automotive executive with extensive experience in vehicle autonomy joined as Professor of Practice two years ago, and allocates ~50 % of his time in mentoring the team of graduate students from diverse disciplines like vehicle dynamics and computer science. As a result, Purdue was just a second behind the seasoned winner PolyMOVE-MSU, a remarkable achievement on this complex racecourse. Per Professor Williams, the factors that contributed to this are: It turns out that the complexity of the Laguna Seca roadway was a perfect fit for what Purdue had been training on under Professor William's guidance for the past 12 months. The Grand Prix event was held 3 days after the IAC robotic car race (24th July), on the same track (Figure 3). This is a Indy 500 racing circuit event consisting of 95 laps (~2.25 miles each) and 27 human-driven race cars, and is part of the NTT INDYCAR Series championship. Experiencing the throb, sounds, smells and sight of engine power equivalent to ~20,000 horses at the start of the race is an out-of-world experience! The winner was Alex Palou, a strong favorite, driving the DHL Chip Ganassi Racing Honda race car (Figure 4). The previous two days included trial competitions. Mr. Palou dominated here as well, and started in the leading position at the Grand Prix event. This is his third win in the past 4 years at this track. Including three pit stops, he took ~2 hours and 5 minutes to cover the 95 laps (~214 miles) at an average speed of 102 mph and reached a maximum average lap speed of ~114 mph in the 10th lap. For reference, Indy racecars can reach maximum speeds of ~240 mph on level, oval tracks like the Indiana Motor Speedway (IMS) in Indianapolis. Given the complexity of the Laguna Seca track, this is considerably lower (~50%). Second and third place went to Arrow McLaren's Christian Lundgaard and Colton Herta of Andretti Global (Figure 5). Lundgaard edged out Herta in an exciting finish in the track's final corner. There were also a few collisions and crashes, and tense moments as officials scrambled to throw flags and clear accidents. Following the Grand Prix event trials a day earlier, and seconds after Alex Palou exited the track, the PoliMOVE-MSU AI driver performed high speed autonomous laps for 10 minutes, exposing thousands of racing fans to the promise of robotic car racing. As mentioned earlier, the PolyMOVE-MSU team won the IAC robotic car event with an average lap time of 90 mph. This is about 80% of that achieved by Mr. Palou. The IAC race was substantially shorter (8 laps), raced a single car at a time., and had a few instances of hardware failures and crashes. Since only a single car performs at any given time, there are no risks of human fatality, multi-car collisions or extensive property damage. IAC racecars have achieved maximum speeds of ~150 mph on the IMS, about 60% that achieved by the Indy 500 cars. Part of the difference can be attributed to the more powerful engine in the latter (700 hp and 6 cylinder engine in the Indy 500 car vs 500 hp, 4 cylinder engine in the IAC car). For Mel Harder, president & general manager, WeatherTech Raceway 'Hosting the IAC at WeatherTech Raceway Laguna Seca as part of the Java House Grand Prix of Monterey was a thrill. Not only did we introduce our fans to the world's fastest autonomous race cars, but IAC also attracted hundreds of companies, researchers, and government leaders in AI and autonomy from Silicon Valley and around the world to our venue, and promoted engagement in motorsports.' Advances in sensing, perception and computing has enabled high-speed F-22 fighter jet pilots achieve substantially higher levels of speed (> Mach 2) and endurance. Similarly, progress in IAC technology (like sensors, perception, compute, vehicle dynamics and active safety) can enable human-driven race cars to achieve higher performance levels, balancing motorsport excitement, audience engagement and human safety. Learning from human race car drivers about multi-agent path planning (local planning) is absolutely critical for physical AI applications like AVs at very high speeds on highways. The ability to use visual, acoustic and localization cues that human drivers employ to operate in multi-agent environments is something that physical AI needs to emulate, on public roadways and racetracks. Human-driven race car performance has plateaued over the last 50 years, because of passive safety protocol constraints and saturation of human driver capability at the top levels of performance. Of course, records will continue to be broken due to factors like weather and performance peaks, as well as changes in car design and race rules. But these improvements are likely to be limited and random. For AI driven race cars, it is a different story. The technology is currently in its infancy, with significant opportunities for improvement as sensor hardware, software, compute stacks and digital twin simulation capabilities accelerate in performance. IAC car performance has improved orders of magnitude in the past 3 years of its existence. The Purdue team demonstrates how experience, physics and physical AI can improve performance dramatically in the space of 12 months. The question is whether Physical AI will improve to the point where robotic and human driven race cars are able to perform together in multi-car track or road racing? To make this a reality, addressing the multi-agent path planning problem is critical. Human drivers are exceptional at this, robotic cars not so much. The success of road traffic applications in which Waymo autonomous cars and human-driven cars perform together in uncontrolled environments depends on humans and computers understanding each other's cues, tactics and behavior (Figure 6). Mixed human and robotic racing will need similar understanding, but at extremely high speeds and very low decision-making latency. Human race car drivers can be instrumental in teaching AI drivers to solve this problem, not by massive data gathering and training, but maybe with other physical AI approaches like neuromorphic learning. Paul Mitchell, CEO of IAC 'hopes that such performance parity and understanding will be achieved in the next 2 decades as IAC and motorsports nurture and learn from each other'.
