I really don't understand why an event like LEL would use chips and timing mats. It's such a hassle compared to just keying a number into a device at a control. That way you need a few dozen devices (a phone will do it) as opposed to a few dozen readers, thousands of chips and bracelets, plus the devices anyway.
I agree that tracking arrival times is easily done as it is now, but the question was about improving estimation of rider arrivals to give the kitchens a chance to ramp up in time and not waste food by having it ready when not enough riders are in. The implication is that rider departure time is more useful which is trickier to reliably obtain from controls.
Mats/chips may not be specifically required, it was just an example of what technology exists now that could help automate the rider tracking (and specifically control exit which currently requires cooperation from the riders).
Having a good idea of where riders are (without GPS trackers) requires:-
a) Details of departure times
b) A good model (basing things on a riders previous speeds between controls, group speeds between controls to account for differing weather/terrain, etc)
Having tracked linger time for a large percentage of riders on LEL2017 it may be possible to create a reasonable model for future LELs based on this data that just requires control arrival times (which are collected like they are now with no extra technology required). From there you might be able to extrapolate to have a reasonable prediction for the bulk of riders.
I'd be interested in the anonymised dataset (including drop bag checkout/checkin) to see what modelling could be done. The idea would be to analyse the complete set to extract behavioural patterns and identify different rider types (stamp and go riders, touring riders, longer stops at bag drop locations. riders up against time limits) and temporal patterns (average stop times at different times of day, average speed drops due to fatigue/terrain, etc) to build up the model. Then replay the minimal dataset (bag drop locations, rider arrival times) in chronological order in an attempt to predict the arrival times of riders at subsequent controls. I could also see how well a model worked against the LEL2013 dataset.
The easiest way to get trackers for an event like LEL is to have something made bespoke - basically a battery attached to a gps chip and a SIM card/SMS chip. But Chinese factories won't get out of bed for fewer than 10,000 devices, so that doesn't work for us either.
There are plenty of trackers that exist that are battery+GPS+SIM, but I'm not sure how easy it would be to get 1500+ of them (including PAYG data only SIM cards). Also the tracker is more expensive if it can't be used for future rides. It has to last 7 or so days on a single battery and the battery has to be replaceable. The service it reports to must have some form of API for bulk API collection, and it has to be reliable (and waterproof)!
The easiest solutions would be to build an app with a very low battery drain, or just give spot trackers to the 5am group.
Unless a mobile is powered/charged by something along the way then not running in airplane mode between controls will drain the battery far quicker than any app. In areas of low/no signal the phone boosts transmission power in the hope of contacting a base station, this is the major battery drain.
It would have to be an app that was capable of periodically disabling airplane mode, waiting for a possible signal, sending position update and then settling back into airplane mode. AIUI airplane mode cannot be disabled by an app.
Even then we have many foreign riders that may find roaming data access on their phone prohibitively expensive and swapping to a local SIM stops them from being in contact with their friends/family.
For those that do power their phone on the go, given the numerous existing apps I can see some service in the future which gathers the data from the various services (Garmin Connect, Strava, FindMyPhone, etc), normalises it, and presents it over one consistent API. Then riders could register with this service providing access to their data to LEL and it is taken from there...