#### Topic: 2021 Lemons Results

I posted this link on the unofficial FB group but figure I should post here too since the traditional forum has more permanence.

Starting mid-summer I began matching the race entries posted by Eric Rood with the final results from speedhive to see how the various cars and models do. I ended up updating the file with all of the prior races and added subsequent races as they have occurred.

**Note **- the data on this file is representative of the results and does NOT include any entries who signed up but didn't make it to race day. I designated team classes as "DNS" (did not start) if teams show up on the results with zero laps.

**Note 2** - I did spend some time cleaning up the data but, as I put this together for fun, I didn't go too crazy. I'm sure some stuff is off here and there and I had to make a few calls on naming conventions because of how some teams spelled this and that (like "Camaro" and "Camero").

**General description of the various sheets**: the green sheets are where the race data is combined and pivots/calculations created/done. The grey sheets show each race's results joined with their entries. The source of ALL of the pivot tables is the "Stacked" sheet.

**Some Sheet-specific explanations**:

**Model % and Manufacture % sheets**- pivot table by model/manufacturer. Includes manufacturer, how many times it was raced ("Model Count"), the average year of manufacture (Average of Year), average finishing position, total overall wins, etc. I added some formulas to the right of the pivot tables showing how each model/manufacturer is weighted and represented in each results category.

**Examples of what the formulas show**:

**From the Model % Sheet**: Miatas made up 8.1% of the raced models, took 15.8% of the overall wins, made up 15.3% of the top ten, 11.6% of the top third, 8.6% of the middle third, and 4.2% of the bottom third. Comparing the percentage of Miatas in the results to the percentages in each category shows they were: overrepresented in the overall wins, top ten, and top third, slightly overrepresented in the middle third and underrepresented in the bottom third.

**From the Manufacturer % Sheet**: Fords made up 13.1% of the raced manufacturers, took 0% of the overall wins, made up 9.5% of the top ten, 13.2% of the top third, 13.6% of the middle third, and 12.4% of the bottom third. Comparing the percentage of Fords in the results to the percentages in each category shows they were: roughly equally represented in the top third and middle third, slightly underrepresented in the bottom third and in the top 10, and totally unrepresented in the overall wins.

**Model by Class & Manufacturer by Class Sheets**: similar data to the % sheets but includes how the cars were classed

**Manufacture Details**: similar data to the Manufacturer by Class Sheet but includes the models.

**Teams Sheet**: this is my favorite sheet by far as it lists all of the teams, which races they attended, how they were classed, what cars they ran, and how they did. The sheer volume of puns employed in the team names is simply staggering.

**Distance Sheet**: this is where I calculate how many racing miles were accumulated over 2021. Almost a million!

**Team Numbers**: I added this one as I was curious which numbers were most commonly used. The answer for 2021 was 11. It was used 17 times in 19 races.

**Stacked Sheet**: this is the source of ALL of the pivot tables. Starting in column R you can see the formulas I used to calculate how each competitor fared against the rest of the field in each race.