retriever - Your spot on here although one better way of defining it statistically is to use FS (Field Size) as a guide where for example if you were using BFSP you can use this metric by converting both Odds and FS to Probabilities ,simply by dividing both into 1 , so ...1/Odds and 1/FS.... then dividing the Odds (as a prob) into FS (as a prob) which spits out a numerical value.
((BFSP(p)/(FS(p)) where small (p)= Probability - spits out a value where 1.00 = Odds = FS as integers (when odds and actual Field Size are converted back from probabilities to numbers) , >=1.00 =Odds are equal or greater than FS as a probability and <1.00 =Odds are less than FS as a probability.
I call it simply (P/p)Ratio and it is very predictive as a metric when used from cumulative past races for horses , trainers, jockeys etc as it is a guide to past expectancy (price) whilst taking an important part (Field Size) into account. There is also a way of compounding "class" of rival making it even more predictive when using these values from past races...eg L3 races , L5 races , L10 races and also Career.
Example

The values can also be used when looking over a series of data - eg a year, just using the values produced from each race and splitting them.
During a typical season using this metric and splitting those that are greater or equal to 1.00...ie Odds(as a probability) are >=FS probability
and those that are less than 1.00..ie (Odds(as a probability) are <FS probability usually works out like this
UK Flat Turf/AW Season 2022 (01/01/22 -31/12/22 inclusive) - 6338 Races

You can see the difference.
Note that "proportion" is a big factor in statistics (which is why Impact Ratios/Values are such good metrics as they account for this)
Those that were priced greater or equal to Field Size (as probabilities) account for only 37.91% of all runners but 72.20% of all winners over the year whilst their opposite - those that were priced less than Field Size (as probabilities) account for 62.09% of all runners but only a measly 27.80% of all winners - this is simple proportion of both runs and wins
Using the Impact Ratio Delta(a simple subtraction of Impact Ratio 1 - Impact Ratio 2) a horse priced(as a Prob) >= Field Size Probability actually has a 147.50% chance of winning than it's opposite and a 90.5% more chance of winning than a random runner (All Runners)
R
rbtblb82 - Whilst i admire anybody attempting to record their own statistics as it is hard graft and tedious work - what is missing from your statistics is basic "Proportion" which is what
pete and
retriever are explaining - what you are doing here is only counting winners and forgetting about the most important of the "strike rate/win%" equation ......
runners...... in most of your statistics .
I would also add that a 49 day sample with this sort of stuff is open to plenty of noise and is no guide to the future, especially just counting wins. Most serious statistic users use maybe 5 years, some 10 and even then we have to account for margin of error, the game adapting and changing over that time,new trainer/jockey/horse/sire/ pools, tracks changing etc. There are techniques involving splitting a large sample into 3 parts mostly involving TTV(Test, Training & Validation) eg a 10 year sample could be split into 4 year "Test" , 3 year "Training" and 3 year "Validation" periods - ideally each part should show some correlation to the other parts.
"proportion" is missing in this part of your post with no account of runners
I don't know your dates of your 49 day sample range but i'll use BFSP(where odds are decimal) but use your same cut-off points in odds ranges (as if they were fractional ISP) BUT also include runners and a few other metrics to show you how the further away the market your playing the more "proportion" counts as the % of runners start to "outweigh" the % of winners for a full years UK Flat Turf/AW racing.
So using the same sample as above, the full year of 2022 UK Flat Turf/AW racing and a sample of 6338 races which includes EVERY runner, EVERY race
It would look like this
Green is where the odds-range beats "par" showed in All runners..........Red - NOT!
And if we graphed % of Runs To % of Wins it would look like this

Note that as both lines approach the >10-1 to <=20-1 range the % of wins dips under the % of runs and the gap between the two lines increases exponentially as the price gets larger - it's simply ......
"PROPORTION"
Note* This is not to say there is not opportunity in the "red" odds-ranges as remember this a full years results as a collective (Btw NH racing shows the same trend as i seen you have included jumps racing in your study) - but caution is advised and indeed i would say extreme selectivity - those who play in this sort of odds-range expect a lowish strike rate, longer losing runs and probably demand an extra premium on top for "Margin of Error" from a "Value" point of view.
Collecting your own statistics is great but most of this stuff is basic and very public knowledge and easily found in most commercial software and what i have showed here is factored into the market and has been for a number of decades now.
What was interesting about your post was where you ventured to look for "Longshot jockeys" ....but again just "counting" wins is meaningless without the other side of the equation - RUNS - having done some of this stuff in the past i reckon Trainers would be a better source to record in this area -"jockey" has more of a random factor to it when it comes to "longshots" but a real study of "Longshot Trainers" can be worthwhile - i will post up a recent study of "LS trainers" but beware when you introduce an odds-range to a factor it is basically a form of "backfitting" so you also need to check for significance - CHI (the racing version of it developed by Steve Tilley) or better Standard Error(SE) are decent metrics to use.