Database

Gravel Rankings

Ranked by ORI Score โ€” an Elo-style rating computed from every graded gravel race in the database, weighted by how much that race counts. Full methodology below.

Current ยท Last 12 Months2026202520242023202220212019
EliteAmateur
Elite Men
1๐Ÿ‡บ๐Ÿ‡ธ Keegan Swenson1107 ORI Score
12 races
2๐Ÿ‡บ๐Ÿ‡ธ Alexey Vermeulen1098 ORI Score
13 races
3๐Ÿ‡บ๐Ÿ‡ธ Payson Mcelveen1083 ORI Score
13 races
4๐Ÿ‡บ๐Ÿ‡ธ Cole Paton1080 ORI Score
11 races
5๐Ÿ‡บ๐Ÿ‡ธ John Borstelmann1075 ORI Score
11 races
6๐Ÿ‡ฆ๐Ÿ‡บ Lachlan Morton1065 ORI Score
8 races
7๐Ÿ‡บ๐Ÿ‡ธ Peter Stetina1061 ORI Score
11 races
8๐Ÿ‡บ๐Ÿ‡ธ Alex Howes1059 ORI Score
12 races
9๐Ÿ‡บ๐Ÿ‡ธ Russell Finsterwald1058 ORI Score
11 races
10๐Ÿ‡ฆ๐Ÿ‡บ Brendan Johnston1056 ORI Score
8 races
11๐Ÿ‡บ๐Ÿ‡ธ Howard Grotts1049 ORI Score
12 races
12๐Ÿ‡บ๐Ÿ‡ธ Zach Calton1041 ORI Score
8 races
13๐Ÿ‡ฎ๐Ÿ‡น Ethan Overson1036 ORI Score
8 races
14๐Ÿ‡ฟ๐Ÿ‡ฆ Matthew Beers1030 ORI Score
5 races
15๐Ÿ‡บ๐Ÿ‡ธ Nathan Spratt1030 ORI Score
9 races
16๐Ÿ‡จ๐Ÿ‡ฆ Adam Roberge1028 ORI Score
9 races
17๐Ÿ‡บ๐Ÿ‡ธ Griffin Easter1026 ORI Score
12 races
18๐Ÿ‡บ๐Ÿ‡ธ Lance Haidet1026 ORI Score
10 races
19๐Ÿ‡บ๐Ÿ‡ธ Bradyn Lange1022 ORI Score
6 races
20๐Ÿ‡ฆ๐Ÿ‡บ Tasman Nankervis1022 ORI Score
8 races
21๐Ÿ‡ฆ๐Ÿ‡บ Freddy Ovett1022 ORI Score
6 races
22๐Ÿ‡ณ๐Ÿ‡ฑ Jasper Ockeloen1021 ORI Score
5 races
23๐Ÿ‡จ๐Ÿ‡ฆ Andrew Lโ€™esperance1020 ORI Score
7 races
24๐Ÿ‡บ๐Ÿ‡ธ Brian Matter1012 ORI Score
5 races
25๐Ÿ‡บ๐Ÿ‡ธ Brennan Wertz1012 ORI Score
11 races
Elite Women
1๐Ÿ‡บ๐Ÿ‡ธ Sofia Gomez Villafane1105 ORI Score
11 races
2๐Ÿ‡บ๐Ÿ‡ธ Lauren De Crescenzo1098 ORI Score
9 races
3๐Ÿ‡บ๐Ÿ‡ธ Alexis Skarda1080 ORI Score
12 races
4๐Ÿ‡บ๐Ÿ‡ธ Paige Onweller1071 ORI Score
15 races
5๐Ÿ‡บ๐Ÿ‡ธ Sarah Sturm1060 ORI Score
9 races
6๐Ÿ‡จ๐Ÿ‡ฆ Haley Smith1060 ORI Score
8 races
7๐Ÿ‡บ๐Ÿ‡ธ Heather Jackson1060 ORI Score
8 races
8๐Ÿ‡บ๐Ÿ‡ธ Sarah Lange1048 ORI Score
5 races
9๐Ÿ‡บ๐Ÿ‡ธ Crystal Anthony1048 ORI Score
10 races
10๐Ÿ‡บ๐Ÿ‡ธ Jenna Rinehart1046 ORI Score
8 races
11๐Ÿ‡บ๐Ÿ‡ธ Flavia Oliveira Parks1045 ORI Score
7 races
12๐Ÿ‡บ๐Ÿ‡ธ Hannah Otto1042 ORI Score
7 races
13๐Ÿ‡บ๐Ÿ‡ธ Emily Newsom1036 ORI Score
8 races
14๐Ÿ‡บ๐Ÿ‡ธ Anna Hicks1036 ORI Score
5 races
15๐Ÿ‡บ๐Ÿ‡ธ Whitney Allison1033 ORI Score
6 races
16๐Ÿ‡บ๐Ÿ‡ธ Deanna Mayles1030 ORI Score
9 races
17๐Ÿ‡บ๐Ÿ‡ธ Rebecca Fahringer1030 ORI Score
7 races
18๐Ÿ‡บ๐Ÿ‡ธ Erin Osborne1021 ORI Score
6 races
19๐Ÿ‡บ๐Ÿ‡ธ Katie Kantzes1019 ORI Score
5 races
20๐Ÿ‡บ๐Ÿ‡ธ Cecily Decker1019 ORI Score
8 races
21๐Ÿ‡บ๐Ÿ‡ธ Emma Grant1018 ORI Score
7 races
22๐Ÿ‡บ๐Ÿ‡ธ Ellen Campbell1017 ORI Score
9 races
23๐Ÿ‡บ๐Ÿ‡ธ Hannah Shell1012 ORI Score
11 races
24๐Ÿ‡บ๐Ÿ‡ธ Isabel King1010 ORI Score
10 races
25๐Ÿ‡บ๐Ÿ‡ธ Tina Hart1008 ORI Score
5 races
Methodology

How the ORI Score works

Every race is a chance to prove how you stack up โ€” not just against whoever else showed up that day, but against the entire sport. A rider who wins their age group at a small local race and a rider who wins their age group at Unbound accomplished very different things. Counting wins or podiums canโ€™t tell them apart. The ORI Score can, because it accounts for exactly who you raced and how much that race counts for.

An Elo rating, built race by race

Chess players have used Elo since 1960 to rate players who never all play each other, using only the games that actually happened. We apply the same math to racing. Every rider starts a ranking period at a baseline of 1000. Then, in chronological order, we replay that periodโ€™s races. For each finisher, we compare them โ€” one at a time โ€” against everyone else in their comparison field:

expected(you vs. rival) = 1 / (1 + 10^((rivalโ€™s score โˆ’ your score) / 400))

Thatโ€™s the probability, given current scores, that youโ€™d beat that specific rival. Average it across the whole field and you get your expected result for that race โ€” essentially, what your rating predicted youโ€™d finish. Then we look at what actually happened:

actual(you) = (finishers behind you) / (total finishers โˆ’ 1)

1st of 50 scores a perfect 1.00; last place scores 0.00. Your rating then moves by:

change = K ร— field-size factor ร— race weight ร— (actual โˆ’ expected)

Beat your prediction and your score climbs; underperform it โ€” even finishing near the front of a field your rating predicted youโ€™d win โ€” and it drops. K is a flat 32 for every race. The field-size factor discounts tiny fields โ€” a 2-person race moves your score far less than a 50-person field would โ€” so an empty parking lot doesnโ€™t count the same as a real championship. The race weight is the ORI Grade below.

Race weight: not every race counts the same

Every graded race carries an ORI Grade โ€” HC, 1, 2, or 3 โ€” reflecting its depth of field and standing in the sport. A strong result at an HC race moves your score more than the identical result at a Grade 3 race would.

HC ร—2.0ย ย ย Grade 1 ร—1.5ย ย ย Grade 2 ร—1.0ย ย ย Grade 3 ร—0.75

Two kinds of ranking period

Current is a rolling window covering your last twelve months of racing โ€” it updates continuously and always reflects recent form. Calendar Year rankings lock in once a year is fully over, as a permanent record of how that season played out. Every rider starts both kinds of period at the 1000 baseline โ€” a Calendar Year ranking is a clean, independent snapshot, not a continuation of the year before, and the Current window resets the same way as it rolls forward.

Only your best 5 results count โ€” but you need at least 5

You need at least 5 results in a division within a period to be ranked at all โ€” enough races to say something real about where you stand. Once you clear that bar, only your 5 best results (the ones that moved your score up the most) count toward your final score for that period; the race count shown next to your score is your full qualifying total, not just those 5.

Elite and Amateur: same math, different comparison basis

For an Eliteresult, youโ€™re scored on your place within the Elite/Pro field itself. For an Amateuror Open result, youโ€™re scored on your place across the entire gender field for that race โ€” Elite, Amateur, and Open riders combined โ€” since thatโ€™s the truest measure of who you actually beat. A race with no Elite/Pro category at all counts as Open, and Open results count toward bothan Elite and an Amateur ranking: a rider with a mix of Elite, Amateur, and Open results in a period can hold both rankings simultaneously. Within Amateur, youโ€™re shown against your own age bracket โ€” assigned once per period, based on your age as of that period, not race by race.

Why this handles field quality automatically

A โ€œfield strengthโ€ number never needs to be computed separately โ€” itโ€™s built into the rating of everyone else on the start line. Beat a field of previously-unrated riders and your score barely moves. Beat a field that includes riders whoโ€™ve already proven themselves against Elite fields elsewhere, and it moves a lot. This is also how an amateur racing in an Open category at a small race that happens to include a pro or two gets full credit for it โ€” the proโ€™s rating (built from their Elite results elsewhere) raises that specific fieldโ€™s difficulty, which raises what the amateur earns for finishing near them.

Why we show a race count next to every score

Elites race the same small pool of people repeatedly across a period, so their ratings converge quickly and reliably. Amateurs in the same age bracket might only cross paths once or twice a year โ€” a score built on 5 races is a lot less certain than one built on 20. Read a fast-rising score with a low race count as promising, not proven.

Current scope & limitations (v2)

  • Gravel and select MTB endurance races that have been individually graded โ€” not every discipline or every race yet.
  • U23 and specialty categories (Single Speed, E-bike, Para, Tandem, Non-Binary) arenโ€™t rated yet โ€” different competitive context, deserving their own pools. Juniors are rated under a single โ€œ18 and underโ€ bucket, regardless of their exact sub-age.
  • DNF/DNS/DSQ results donโ€™t affect your score at all right now โ€” a deliberate simplification, not a permanent design choice.
  • Exact-tie finishes are broken arbitrarily rather than treated as a true tie โ€” a minor imprecision.
  • A small number of riders have duplicate profile records in our matching pipeline (the same person imported under two different IDs). Until thatโ€™s cleaned up, their race history โ€” and their score โ€” can be split across both.