ThePredictionTracker NCAA RESULTS

Computer Rating System Prediction Results for College Football (NCAA IA)

2022 Last Week

Through 2023-01-10

Rank	System	Pct. Correct	Against Spread	Absolute Error	Bias	Mean Square Error	games	suw	sul	atsw	atsl
1	Line (Midweek)	0.64286		12.1667	-1.4524	247.795	42	27	15
2	Brent Craig	0.66667	0.66667	22.3433	-10.6167	841.238	3	2	1	2	1
3	Line (updated)	0.61905	0.68000	12.0357	-1.2024	239.245	42	26	16	17	8
4	Computer Adjusted Line	0.61905	0.64286	12.0119	-1.2262	243.613	42	26	16	18	10
5	DP Dwiggins	0.57143	0.65789	11.8810	0.0714	267.738	42	24	18	25	13
6	Talisman Red	0.61905	0.65854	11.7921	-0.5493	266.059	42	26	16	27	14
7	System Average	0.61905	0.65854	11.8624	-0.9576	263.357	42	26	16	27	14
8	Keeper	0.63415	0.65000	11.2166	0.5961	220.620	41	26	15	26	14
9	System Median	0.61905	0.63415	11.8533	-1.0600	263.523	42	26	16	26	15
10	Pi-Ratings Mean	0.64286	0.63415	12.0810	-0.6762	266.693	42	27	15	26	15
11	Versus Sports Simulator	0.61905	0.63415	11.9095	-0.3662	271.862	42	26	16	26	15
12	Line (opening)	0.59524	0.57143	12.2857	-1.6190	270.676	42	25	17	20	15
13	Sagarin Points	0.57143	0.63415	11.8288	-0.9998	264.123	42	24	18	26	15
14	Waywardtrends	0.69048	0.63415	11.6438	-1.2486	259.150	42	29	13	26	15
15	PI-Rate Bias	0.61905	0.60976	12.2288	-0.4093	264.325	42	26	16	25	16
16	PerformanZ Ratings	0.59524	0.60976	11.8736	-0.4179	242.434	42	25	17	25	16
17	Laz Index	0.57143	0.60976	11.7760	-0.9650	264.507	42	24	18	25	16
18	Linear Regression	0.61905	0.60976	11.8124	-0.1338	267.945	42	26	16	25	16
19	Catherwood Ratings	0.73810	0.56757	12.0476	-0.8095	275.699	42	31	11	21	16
20	Payne Predict	0.61905	0.60976	11.7355	-0.8664	283.301	42	26	16	25	16
21	Stat Fox	0.69048	0.60000	11.6669	-1.2374	272.594	42	29	13	24	16
22	TeamRankings.com	0.64286	0.60000	11.5643	-0.7357	259.951	42	27	15	24	16
23	Stephen Kerns	0.64286	0.60976	11.3655	-0.8940	246.748	42	27	15	25	16
24	Sagarin Recent	0.69048	0.60976	11.8129	-1.2805	262.814	42	29	13	25	16
25	Sagarin Ratings	0.61905	0.60976	11.8360	-1.2274	264.261	42	26	16	25	16
26	Pigskin Index	0.69048	0.57895	12.1193	-1.0236	270.238	42	29	13	22	16
27	Pi-Rate Ratings	0.66667	0.58537	12.2095	-0.7714	269.618	42	28	14	24	17
28	Donchess Inference	0.57143	0.58537	12.0095	-1.4857	259.430	42	24	18	24	17
29	Dave Congrove	0.61905	0.58537	12.0848	-2.3629	282.688	42	26	16	24	17
30	Payne Power Ratings	0.52381	0.56098	12.4612	-1.5850	283.672	42	22	20	23	18
31	Massey Ratings	0.59524	0.56098	12.4081	-1.5595	284.297	42	25	17	23	18
32	FEI Projections	0.57143	0.53659	12.2050	0.0764	260.699	42	24	18	22	19
33	Dokter Entropy	0.54762	0.53659	12.0024	-0.7848	271.779	42	23	19	22	19
34	Howell	0.57143	0.51282	12.8571	-0.7143	271.785	42	24	18	20	19
35	Cleanup Hitter	0.50000	0.53659	13.1786	-2.3210	319.181	42	21	21	22	19
36	Moore Power Ratings	0.59524	0.53659	12.5036	-0.7512	303.554	42	25	17	22	19
37	ESPN FPI	0.59524	0.53659	11.6314	-1.2129	264.299	42	25	17	22	19
38	David Harville	0.61905	0.53659	11.8800	-0.7919	271.192	42	26	16	22	19
39	Edward Kambour	0.66667	0.53659	12.4590	-0.1914	272.272	42	28	14	22	19
40	Daniel Curry Index	0.73171	0.52500	12.5902	-1.0293	275.927	41	30	11	21	19
41	Least Squares w/ HFA	0.59524	0.51220	12.9340	2.8545	278.387	42	25	17	21	20
42	Beck Elo	0.59524	0.48718	12.2888	-1.6202	283.215	42	25	17	19	20
43	Sagarin Golden Mean	0.57143	0.51220	12.2471	-1.3338	278.335	42	24	18	21	20
44	Born Power Index	0.69048	0.51220	12.6929	-0.8786	302.962	42	29	13	21	20
45	Logistic Regression	0.50000	0.51220	12.8690	-2.3814	312.138	42	21	21	21	20
46	ARGH Power Ratings	0.54762	0.47500	12.4464	-1.1250	267.153	42	23	19	19	21
47	Massey Consensus	0.54762	0.48780	12.7398	-1.7383	305.898	42	23	19	20	21
48	Dunkel Index	0.69048	0.48780	12.5607	-0.4536	280.539	42	29	13	20	21
49	Billingsley	0.50000	0.48780	12.7510	-2.4162	292.733	42	21	21	20	21
50	Laffaye RWP	0.54762	0.43902	13.0069	1.1355	284.436	42	23	19	18	23
51	Payne W/L	0.52381	0.43902	12.9883	-1.9721	301.727	42	22	20	18	23


* This system does not make predictions.  I make predictions for this
  system by translating it to a new scale that allows for making predictions.



Retrodictive records are found by taking the ratings from the current week
and applying them to the entire season to date.

The ideal system would be one that has the highest correct game decisions,
has the smallest mean error(deviation from the actual game result), and has
a bias of zero.

Mean Error = average[abs(prediction-actual)]

      Bias = agerage(prediction - actual)

      Std. = Standard Deviation of individual game biases