Report on my Dereham 10 Mile race, where I stuck to my strategy and achieved a big personal record.
Last Sunday (14th May 2023), I ran the Dereham 10 Mile. As mentioned in my pre-race article, this is a 10 mile road race hosted by Dereham Runners Athletics Club starting / finishing in the town of Dereham (Norfolk, England) and taking in some of the small villages and hamlets to the north east of the town.
I had a dilemma
Being a hyper-local event, I had the luxury of having an extra lie-in and a relaxed morning before needing to make my way to the race HQ. Whilst it was nice to have a relaxed morning, I obviously took it to the extreme. So much so that upon arriving at the event I realised that I had forgotten my contact lenses. And whilst I do virtually all of my training runs with glasses on, I really don’t like to race in them, so I had a dilemma. I opted to race glasses free. So apologies now to anyone who I did not acknowledge as I could not see you!
I was thinking of nothing but my 4:10 min/km pace
Following the pre-race talk we walked the hundred or so metres from the race HQ to the start line on the road. The temperature was perfect at 16 °C with little to no wind and I made my way through the crowd of runners to be relatively near the start, where I found a couple of running friends. I felt in good shape and was thinking of nothing but my intended 4:10 min/km pace, and hitting the bottom of the hill at the turn-around point at around 39 minutes.
Commonly with this race, there is a little delay whilst the roads are confirmed clear. But not on this occasion, and before I knew it, we were off.
My Race
I had got off to a perfect start
To the spectators, it would have appeared that I was off to a slow start, as during that first few hundred metres my fellow runners seemed to surge past me. I was confident in my own pace, but did check my watch to sanity check that something was not a miss. It confirmed that I was around 4:05 min/km, so I resisted the urge to follow the crowd, completing the first kilometre in a 4m04s. I had got off to a perfect start, and I could now settle in for 8.5 km of gentle downhill. The kilometres passed by: 4:00, 4:02, 4:06, 4:07… I was feeling relaxed and comfortable. So comfortable that I had to consciously ensure that I did not get ahead of myself and over-cook the downhill section. I was soon at the steepest sections and threw in another couple of 4:00 min/km stages.
I was 1 second ahead of my 67 minute schedule
I turned the corner and arrived at my personal 9.5 km marker. My watch read 38m44s. I was 1 second ahead of my 67 minute schedule, and pleased with how I felt. I hit the 10 km point when the hills start to get a little steeper. Only 6 kilometres to go! I was determined to push a little up the hills and not let my 67 minute time slip away. At 13 kilometres there is a little lull in the incline, where I was still feeling strong and allowed myself to up my effort level. There’s only 3 km to go until the final downhill km. What can go wrong! I was now concentrating so hard on pushing myself that the maths of where I was in relation to my schedule was out the window. I knew that I had not gone over 4:20 min/km, and so confident that I was on, if not ahead, of schedule.
Time to let the hill take me home!
I hit the 15 km point and it was all downhill now to the finish! My watch read 1m02m – without my glasses I couldn’t see the seconds. Time to let the hill take me home! I ran for all I was worth. My legs felt amazingly good, but my lungs were struggling and I tried all I could to get as much oxygen in as possible. I dug deep and kept going, with the crowds lifting me to finish with a final pace of 3:36 min/km! My watch read 1h06m36s. I managed to stay standing, but it took a while to catch my breath and be in a position to respond coherently to people around me.
Official chip time 66m33s! Six minutes faster than last year, and a big personal best!
Analysis
Below is a table illustrating the various predictions for my race, along with their error from my actual performance.
Algorithm | Prediction Pace (min/km) | Predicted Time (mm:ss) | Error (seconds) | Error (percent) |
Riegel (3.2 km Assessment) | 4:47 | 77:01 | + 628 | + 13.6 % |
Riegel (6 min Assessment) | 4:46 | 76:54 | + 621 | + 13.4 % |
Running Watch | * 4:22 * | * 70:20 * | + 227 | + 5.4 % |
Data Analysis Platform | * 4:15 * | * 68:28 * | + 115 | + 2.8 % |
TrainAsONE | 4:11 | 67:21 | + 48 | + 1.1 % |
Riegel (10 km Race) | 4:10 | 67:00 | + 27 | + 0.7 % |
As expected from my previous races, the Riegel estimates based upon my assessment runs were highly inaccurate.
At over 5% error, the prediction from the Running Watch was probably in the area of not being too helpful. And with an error almost half of this, the Data Analysis Platform fared much better, providing a reasonable predicted pace.
TrainAsONE’s prediction was only 1 % out
At well under half the error again, TrainAsONE’s prediction was only a percentage point out. And, along with the race-based Riegel prediction, provided extremely useful insight into a racing strategy. And talking of racing strategy, my second half running equated to a 3.5% drop in performance – much better than last years 10%!
Finishing Up
The clear message is that TrainAsONE predicted a significantly more accurate time (again) than the other ‘intelligent systems’. Additionally, it’s advantage over Riegel of not requiring a previous race in order to make its calculation is extremely beneficial, and cannot be under-estimated.
The home advantage contributed to a great performance
Additionally, I believe a significant contributing factor to my ‘stella performance’ was the home advantage phenomenon. Familiarity of the course from previous times racing it, and knowing a number of the marshals (who cheered me on), must have taken 48 seconds off of my time 😉
I’ve now got just over 4 weeks to my next race. It’s another 10 km. So a decent enough block of training in preparation for another PB attempt…
As ever, a big thank you to the race organisers, volunteers and all those involved. A great race. Thank you.
Till next time.