I always get a chuckle when I receive an email with “race-winning data” to look at, as if it is somehow different or special compared to, well…data that didn’t win the race. It makes me smile because quite often there is not much to be learned from such data, contrary to what we logically think would be the case.
An example of this is the recent Mopar CSBK round at Atlantic Motorsport Park, where Jodi Christie won the Superbike race on his Accelerated Technologies Honda CBR1000RR. The race played out with Jodi leading for most of the laps, with Jordan Szoke shadowing him. The last couple of laps were a flurry of activity with several passes. For most of the race, Jodi and Jordan were turning laps two seconds a lap slower than their qualifying times; both knew it would come down to the last couple of laps, so there was no rush and no point for either of them to show their cards early. It’s doubtful there is much to be learned from the data of those early laps at that slower pace.
The pace was definitely quicker in the last couple of laps, but now Jodi was concentrating more on protecting his line when in the lead, or finding a way past when he was following. Data from these laps is potentially of more use, but the context definitely needs to be considered if action is to be taken based on that data. As it turns out, the team had removed the AiM EVO4 system from the bike just before the race due to a cracked mounting bracket so we only have GPS data from the Solo lap timer to use. Based on how the race turned into that cat-and-mouse game, the missing data is most likely not a big loss.
Last year’s Superbike national at Autodrome St-Eustache provides a good example of how race data can be much more useful. Here, Jodi ran lap times much closer to his qualifying time for a good portion of the race as he chased Jordan and Alex Welsh, eventually finishing second to Jordan. Looking at the GPS position data (giving Jodi’s riding line) and segment times from that long run of laps let us piece together some ideas for a quicker way through the turn 1-2 chicane, which Jodi was able to try at this year’s event. Win or lose, the usefulness of the data depends more on how the race played out rather than anything else.
At the very opposite end of the spectrum from the Superbike race data at AMP is the data from the Hindle Pro Sport Bike race, where Jodi crashed on the first lap. It can be pretty disheartening to look at data from a crash (and I would much rather look at race-winning data) but as always there is something to be learned from analyzing what actually happened. What went wrong? Did we miss something on the setup, or was the rider doing something different than previous laps?
The attached graphic shows some of the data we have from the incident at AMP. On the left are two GPS traces of Jodi's riding line through turn 8 of AMP, a fast left-hander. The blue trace is a qualifying lap, while the red trace is the first lap of the race. On the right are matching speed traces for the same portion of the track, with the same colours.
You can see that things start to go awry at about the 48-second mark on the speed traces, where they diverge. In most crashes, speed rapidly drops to zero as the bike slides to a stop, but here Jodi hung on for a long time and tried his best to stay on the bike. Six seconds later, speed takes a big drop and tapers to zero when the bike and Jodi finally hit the ground. Luckily, Jodi was not seriously hurt in the crash at AMP, and even rebounded to win the Superbike race later in the day.
The AiM Solo and EVO4 record lateral and longitudinal acceleration values, and from this data it's possible to derive values for front and rear wheel loading, something that I have discussed previously as being important for looking at data from a safety standpoint. Additionally we can look at the actual riding line or any of the other GPS information to see what’s different from previous laps. At AMP, the only anomaly is Jodi’s line through the turn; all the acceleration and loading values fall within their typical ranges for that section. This would indicate that there is some characteristic of the track that makes line choice critical for how hard Jodi normally accelerates in that particular corner.
The EVO4 system on the superbike is more elaborate and can shed more light on the circumstances that led to a crash. Last year at Canadian Tire Motorsport Park (Mosport), Jodi crashed on the superbike in Saturday’s race. We made some adjustments for the Sunday race last year, but over the winter I was able to take a more in-depth look at the crash data – in particular the suspension data - and plan some more setup changes for this year’s event at CTMP. That takes place this weekend and is the final round of this year’s series. We will see how well those changes work.
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