AccuPredict Driver Projection Results

NASCAR Xfinity Series

This is a historical results' page analyzing Accupredict's accuracy in driver projections. A novel idea, right? Checking how accurate an experts projections are.

Alsco Uniforms 300 @ Charlotte Motor Speedway

Race #13 of the 2022 NASCAR Xfinity Series Season - May 28th, 2022.

Picked Race Winner? NO

TOP-10

70%

TOP-20

70%

MID-TIER

30%

LOW-TIER

25%

Driver	FIN	RNK	RNK +/-	PRJ FIN	PRJ +/-
AJ Allmendinger	19	3	16	10.10	8.90
Anthony Alfredo	33	21	12	21.35	11.65
Austin Dillon	31	17	14	17.06	13.94
Austin Hill	14	10	4	14.59	-0.59
Bayley Currey	15	31	-16	25.46	-10.46
Brandon Brown	17	23	-6	22.15	-5.15
Brandon Jones	16	5	11	11.42	4.58
Brennan Poole	35	34	1	26.56	8.44
Brett Moffitt	11	15	-4	16.34	-5.34
CJ McLaughlin	23	42	-19	31.35	-8.35
Daniel Hemric	6	9	-3	13.44	-7.44
Jeb Burton	12	13	-1	14.92	-2.92
Jeffrey Earnhardt	37	36	1	26.83	10.17
Jeremy Clements	22	18	4	19.45	2.55
JJ Yeley	18	32	-14	26.17	-8.17
Joe Graf Jr	34	28	6	24.50	9.50
Josh Berry	1	7	-6	12.40	-11.40
Josh Williams	27	26	1	23.01	3.99
Justin Allgaier	7	1	6	8.32	-1.32
Kyle Weatherman	36	22	14	21.72	14.28
Landon Cassill	29	19	10	19.70	9.31
Matt Mills	20	41	-21	29.71	-9.71
Myatt Snider	10	25	-15	22.95	-12.95
Nicholas Sanchez	28	37	-9	27.07	0.93
Noah Gragson	4	2	2	9.58	-5.58
Riley Herbst	25	12	13	14.90	10.10
Ryan Ellis	13	27	-14	24.16	-11.16
Ryan Preece	5	14	-9	15.01	-10.01
Ryan Sieg	32	11	21	14.77	17.23
Ryan Vargas	26	33	-7	26.37	-0.37
Sam Mayer	3	6	-3	12.12	-9.12
Shane Lee	24	29	-5	25.12	-1.12
Sheldon Creed	8	16	-8	16.36	-8.36
Stefan Parsons	30	24	6	22.27	7.73
Timmy Hill	38	30	8	25.28	12.73
Trevor Bayne	9	4	5	10.67	-1.67
Ty Gibbs	2	8	-6	12.53	-10.53

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View Accupredict's Projection History Performance by a helpful variety of groupings to better identify the tracks and drivers that it projects the best.

Accupredict Pages

Accupredict is my exclusive NASCAR driver finish position projection tool for Crew Chief and Owner subscribers. The projection algorithm is the work of research scientist Cliff DeJong using traditional and loop data statistics. I've had it online since the 2012 Daytona 500.

NASCAR CUP SERIES

NASCAR XFINITY SERIES

ACCUPREDICT ARTICLES by Cliff DeJong

The driver, track and race links in the tables link to more historical Accupredict data.

How I Calculate Difference (+/-)

Differences are calculated as: Actual Finish minus Projection

Which 'direction' the difference 'moves' is important. A difference that is negative means the driver finished better than projected. A difference that is positive means he finished worse than projected. A zero means Accupredict got the drivers finish position spot on.

Driver was projected to finish 12th. If the difference is calculated using the Absolute Value of the number (the number of positions 'off' without classifying the number positive or negative) then you're getting a range of eight positions. Four of which are worse than the projected finish.

In my experience, differences between +2 to -4 consistently, are the 'sweet spot' for favorable use of a driver based on Accupredicts projection.

When I show the Absolute Value (distance from zero that a number is on the number line, without considering direction. Absolute Value is always a positive number.) value of a number in addition to Real value.

The MEDIAN and the MEAN numbers in the sidebar both use the Absoulte Value of the number difference between Rank/Predicted Finish and Actual Finish. Otherwise, the positive and negative differences cancel each other out and calculate to 0. I show the STANDARD DEVIATION and the FREQUENCY TABLE using both the absolute and real numbers and note there which is being shown.

I've watched over the years, Accupredict is more conservative in projecting a drivers finish and thus has more negative differences. That is, projecting a driver to finish 18th - they finish 14th - resulting in a difference of (ACTUAL - RANK) 14 - 18 = -4. Whereas, the other way around is a +4. To me, knowing that I missed the projection of a driver by 4 spots...and they were 4 spots better, is a lot better than being 4 spots worse. One way I get more points, the other way I get fewer.

Also, the large positive numbers are the result of a 'top-tier' driver ranked in the top-10 being the victim of a wreck, mechanical failure or unfortunate timed caution.

USING ABSOLUTE VALUES

8.68 Mean (Average) number of positions 'off' that the projections were.

7.00 Median (the middle number when all are sorted, descending) number of 'positions off' that the projections were. Median helps account for outliers in the sequence that might skew the Mean with such a small population of data.

5.68 Standard Deviation measures the dispersion of each driver 'positions off' in relationship to the Mean (Average).

The Frequency table below shows the distribution of values (Accupredict projection vs actual finish position) projections were 'off by'.

VALUE	FREQUENCY	%
1	4	10.81%
2	1	2.70%
3	2	5.41%
4	3	8.11%
5	2	5.41%
6	6	16.22%
7	1	2.70%
8	2	5.41%
9	2	5.41%
10	1	2.70%
11	1	2.70%
12	1	2.70%
13	1	2.70%
14	4	10.81%
15	1	2.70%
16	2	5.41%
19	1	2.70%
21	2	5.41%

USING INTEGER VALUES

10.36 When the Standard Deviation is large, look at the Frequency Table for outliers (a projection that is way off) to see if that has skewed the numbers. The larger the difference in range of numbers, the larger the Standard Deviation will be.

The Frequency table below shows the distribution of values (Accupredict projection vs actual finish position) projections were 'off by' using both positive and negative numbers.

VALUE	FREQUENCY	%
-21	1	2.70%
-19	1	2.70%
-16	1	2.70%
-15	1	2.70%
-14	2	5.41%
-9	2	5.41%
-8	1	2.70%
-7	1	2.70%
-6	3	8.11%
-5	1	2.70%
-4	1	2.70%
-3	2	5.41%
-1	1	2.70%
1	3	8.11%
2	1	2.70%
4	2	5.41%
5	1	2.70%
6	3	8.11%
8	1	2.70%
10	1	2.70%
11	1	2.70%
12	1	2.70%
13	1	2.70%
14	2	5.41%
16	1	2.70%
21	1	2.70%