Winter '12 / '13 NEWxSFC - Interim Standings…after Storm #4					AVG SUMSQ				AVG STP					AVG Total Absolute				AVG Absolute				Mean RSQ
Previous Ranks	Rank	Forecaster	Class	Total STN 4casts	Error (")	Error Z	% MPRV over AVG	Rank	4cast (")	Error	Error Z	% MPRV over AVG	Rank	Error (")	Error Z	% MPRV over AVG	Rank	Error (")	Error Z	%MPRV over AVG	Rank	RSQ	RSQ Z	% MPRV over AVG	Rank	Forecaster
3	1	Brad Yehl	Journeyman	68	72	-0.983	64%	2	56	6	-0.820	69%	3	24.7	-1.179	43%	2	1.1	-1.327	43%	1	79%	1.178	38%	2	Brad Yehl
5	2	herb @maws	Senior	66	338	-0.774	53%	3	126	15	-0.385	35%	5	53.3	-0.644	25%	4	2.4	-0.629	22%	4	81%	0.848	17%	4	herb@maws
1	3	dryslot	Intern	65	241	-0.753	48%	2	130	15	-0.466	44%	6	42.8	-0.783	28%	4	1.9	-0.719	23%	4	82%	1.119	19%	3	dryslot
2	4	donsutherland1	Chief	66	402	-0.665	38%	4	120	26	-0.207	16%	8	61.8	-0.699	21%	5	2.8	-0.660	19%	5	70%	0.762	29%	4	donsutherland1
4	5	Donald Rosenfeld	Senior	67	96	-0.639	41%	4	61	4	-0.868	73%	2	29.4	-0.763	28%	3	1.3	-0.784	27%	3	66%	0.539	12%	6	Donald Rosenfeld
9	6	snocat918	Rookie	67	462	-0.328	19%	7	122	15	-0.487	41%	5	59.0	-0.363	12%	6	2.7	-0.302	9%	6	69%	0.239	13%	6	snocat918
12	7	weatherT	Senior	69	499	-0.233	18%	7	145	25	-0.366	27%	8	67.4	-0.039	3%	9	2.9	-0.072	3%	8	61%	-0.630	-11%	10	weatherT
6	8	iralibov	Senior	73	309	-0.131	2%	6	148	20	0.147	-5%	6	61.3	0.292	-14%	7	2.6	0.146	-8%	6	58%	0.013	-1%	6	iralibov
7	9	Roger Smith	Senior	70	355	-0.129	4%	6	157	24	0.020	6%	7	59.7	0.050	-5%	7	2.6	-0.007	-1%	7	69%	0.050	-1%	5	Roger Smith
13	10	TQ	Senior	68	878	0.557	-35%	10	102	66	0.915	-87%	11	81.8	0.173	-7%	9	3.5	0.170	-7%	9	55%	-0.411	-2%	9	TQ
14	11	quagmireweathercentral	Rookie	67	1338	1.612	-92%	11	213	84	1.005	-96%	9	116.6	1.762	-56%	11	5.0	1.954	-60%	12	33%	-1.665	-48%	12	quagmireweathercentral

There have been four (4) snowstorm forecasting Contests…as of 17-FEB-13. Under the ‘two-thirds’ rule…forecasters who have entered at least three (3) forecasts are included in this interim summary.

To qualify for ranking in the Interim and final ‘End-of-Season’ standings…a forecaster must enter at least two-thirds of all Contests.  If a forecaster has made more than enough forecasts to qualify for ranking…only the lowest SUMSQ Z-scores necessary to qualify are used in the computing the average.  IOW…if you made nine forecasts…only your six best SUMSQ Z-scores are used to evaluate your season-to-date performance.  You can think of it as dropping the worse quiz score before your final grade is determined.  The reason we have this rule is to 1) make it possible to miss entering a forecast or two throughout the season and still be eligible for Interim and ‘End-of Season’ ranking and 2) encourage forecasters to take on difficult and/or late-season storms without fear about how a bad forecast might degrade their overall 'season-to-date' performance score(s).

The mean normalized ‘SUMSQ error’ is the Contest/s primary measure of forecaster performance.  This metric measures how well the forecaster/s expected snowfall 'distribution and magnitude' for _all_ forecast stations captured the 'distribution and magnitude' of _all_ observed snowfall amounts.  A forecaster with a lower average SUMSQ Z Score has made more skillful forecasts than a forecaster with higher average SUMSQ Z Score.

The 'Storm Total Precipitation error’ statistic is the absolute arithmetic difference between a forecaster/s sum-total snowfall for all stations and the observed sum-total snowfall. This metric…by itself…is not a meaningful measure of skill…but can provide additional insight of forecaster bias.

The 'Total Absolute error' statistic is the average of your forecast errors regardless of whether you over-forecast or under-forecast. This metric measures the magnitude of your errors.

The 'Average Absolute Error' is the forecaster/s ‘Total Absolute Error’ divided by the number of stations where snow was forecast or observed.

The ‘RSQ error’ statistic is a measure of the how well the forecast captured the variability of the observed snowfall. Combined with the SUMSQ error statistic…RSQ provides added information about how strong the forecaster/s ‘model’ performed.