2017 Round 31 Expected Player Points – *SPECIAL EDITION* (almost) ALL Expected Points!

Every week, I’ll be publishing a list of top players ranked by expected offensive points. This week, I’m publishing a special edition of my projections which includes expected values for (almost)¹ all player points, not just offensive points!  This includes expected goals & assists, shots, expected clean sheets and negative points from goals against, bonus points, and even points differentiated by position (e.g., defender gets 6 points per goal scored, forward gets 5).

Want to know more about how this model works? Click here to read the inaugural article in this series, along with an explanation of the model’s basic premise.

Round 31 Expected Player Points

First	Last	Team	Pos	Price	Total xPt	Price/xPt	Min	Shtp96	xGp96	KPp96	xAp96	RdGamePts	BPp96	xBP	Pos xCS
Benny	Feilhaber	SKC	M	10.8	11.9	0.91	2247	2.26	0.23	2.48	0.25	4.0	1.5	2.9	0.60
Josef	Martinez	ATL	F	9.8	11.9	0.83	1254	4.44	0.85	0.92	0.08	2.0	0.3	0.3	0.00
Jimmy	Medranda	SKC	D	5.9	11.4	0.52	1766	2.17	0.11	1.47	0.13	4.0	1.2	2.4	2.21
Michael	Boxall	MN	D	4.3	11.0	0.39	856	0.11	0	0.11	0	4.0	3.6	7.2	-0.19
Kevin	Molino	MN	M	10.2	10.3	0.99	2350	1.76	0.32	1.55	0.17	4.0	1.1	2.3	0.27
Diego	Rubio	SKC	F	7.1	10.3	0.69	733	2.62	0.34	1.57	0.18	4.0	0.7	1.3	0.00
Matt	Besler	SKC	D	7.4	10.1	0.73	2287	0.17	0.02	0.29	0.07	4.0	1.6	3.2	2.21
Ike	Opara	SKC	D	6.4	10.0	0.64	2389	0.52	0.06	0.28	0.02	4.0	1.4	2.9	2.21
Gerso	Fernandes	SKC	M	8.2	9.8	0.83	2107	2.64	0.29	1.41	0.14	4.0	0.5	0.9	0.60
Latif	Blessing	SKC	F	7.3	9.8	0.75	1211	2.38	0.37	1.51	0.08	4.0	0.6	1.3	0.00
Francisco	Calvo	MN	D	6.3	9.7	0.65	2198	0.7	0.08	0.17	0.01	4.0	2.4	4.9	-0.19
	Ibson	MN	M	7	9.4	0.74	2327	0.58	0.02	1.03	0.08	4.0	2.2	4.4	0.27
Saad	Abdul-Salaam	SKC	D	5.2	9.2	0.56	780	0.86	0.14	0.98	0.05	4.0	0.5	1.0	2.21
Michael	Murillo	NYRB	D	5.6	9.0	0.62	578	1.49	0.27	0.66	0.06	2.0	3.2	3.2	1.23
Miguel	Almiron	ATL	M	10.5	9.0	1.17	2489	3.28	0.29	1.93	0.23	2.0	1.2	1.2	0.39
Johan	Venegas	MN	M	7.3	8.7	0.84	1253	2.38	0.19	1.69	0.22	4.0	0.5	1.1	0.27
Sam	Nicholson	MN	M	7.9	8.2	0.96	554	2.43	0.19	1.56	0.14	4.0	0.5	1.0	0.27
Daniel	Salloi	SKC	F	4.5	8.2	0.55	906	3.28	0.3	0.64	0.03	4.0	0.1	0.2	0.00
Christian	Ramirez	MN	F	7.8	8.2	0.96	2104	2.42	0.4	0.5	0.05	4.0	0.1	0.3	0.00
Ethan	Finlay	MN	M	8.5	8.1	1.05	1921	1.55	0.24	0.9	0.13	4.0	0.4	0.9	0.27
Jerome	Thiesson	MN	D	5.0	8.1	0.62	2651	0.33	0.01	0.65	0.08	4.0	1.8	3.6	-0.19
Ismaila	Jome	MN	M	4.3	8.0	0.53	721	0.13	0.03	0.8	0.11	4.0	1.5	2.9	0.27
Tim	Melia	SKC	G	5.8	8.0	0.72	2777	0	0	0.1	0.01	4.0	0.9	1.7	2.21
Abu	Danladi	MN	F	5.3	7.9	0.67	1177	2.53	0.31	1.06	0.07	4.0	0.2	0.5	0.00
Seth	Sinovic	SKC	D	5.4	7.7	0.70	2652	0.43	0.02	0.43	0.03	4.0	0.5	0.9	2.21
Hector	Villalba	ATL	F	10	7.7	1.30	2375	2.83	0.31	1.25	0.18	2.0	0.7	0.7	0.00
Jermaine	Taylor	MN	D	4.3	7.7	0.56	572	0.34	0.02	0.67	0.04	4.0	1.7	3.4	-0.19
Roger	Espinoza	SKC	M	6.6	7.6	0.86	2352	1.39	0.06	0.98	0.08	4.0	0.7	1.5	0.60
Ilie	Sanchez	SKC	M	7	7.4	0.95	2683	0.54	0.03	0.68	0.04	4.0	1.0	2.1	0.60
Miguel	Ibarra	MN	M	7.7	7.2	1.07	1656	1.16	0.12	1.22	0.14	4.0	0.5	0.9	0.27
Sacha	Kljestan	NYRB	M	11.6	7.1	1.63	2427	1.42	0.09	3.72	0.37	2.0	2.1	2.1	0.32
Leandro	Gonzalez Pirez	ATL	D	7.5	7.0	1.07	2588	0.59	0.06	0.22	0.02	2.0	2.3	2.3	1.72
Daniel	Royer	NYRB	M	8.1	6.9	1.17	1673	2.58	0.44	0.98	0.13	2.0	0.6	0.6	0.32
Bradley	Wright-Phillips	NYRB	F	10.6	6.9	1.54	2404	3.03	0.52	1.16	0.12	2.0	0.4	0.4	0.00
Sam	Cronin	MN	M	6.5	6.9	0.94	1914	0.35	0.01	0.3	0.04	4.0	1.1	2.2	0.27
Collen	Warner	MN	M	6.2	6.8	0.91	1151	0.58	0.02	0.42	0.01	4.0	1.1	2.2	0.27
Greg	Garza	ATL	D	6.3	6.8	0.93	2223	0.52	0.05	1.47	0.19	2.0	1.1	1.1	1.72
Yamil	Asad	ATL	M	8.8	6.8	1.30	2453	1.8	0.13	1.6	0.19	2.0	1.3	1.3	0.39
Justin	Davis	MN	D	4.2	6.7	0.63	533	0	0	0.18	0.07	4.0	1.3	2.5	-0.19
Julian	Gressel	ATL	M	4.4	6.4	0.68	1679	1.32	0.12	1.43	0.22	2.0	1.0	1.0	0.39
Kevin	Venegas	MN	D	4.2	6.4	0.65	573	0	0	0.5	0.05	4.0	1.2	2.3	-0.19
Erick	Torres	HOU	F	8.6	6.4	1.35	1929	3.38	0.65	1.69	0.13	2.0	0.5	0.5	0.00
Kemar	Lawrence	NYRB	D	5.2	6.2	0.84	1960	0.78	0.08	0.69	0.15	2.0	1.5	1.5	1.23
Brent	Kallman	MN	D	4.6	6.1	0.75	1863	0.26	0.06	0.1	0.01	4.0	0.8	1.6	-0.19
Aaron	Long	NYRB	D	5.6	6.1	0.92	2543	0.79	0.1	0.04	0	2.0	1.9	1.9	1.23
Alberth	Elis	HOU	F	8.5	6.0	1.41	1419	3.52	0.44	1.15	0.24	2.0	0.7	0.7	0.00
Graham	Zusi	SKC	na		6.0	0.00	1959	1.52	0.06	2.16	0.14	4.0	0.0	0.0	0.00
Mauro	Manotas	HOU	F	7.9	6.0	1.32	1909	3.22	0.53	1.66	0.19	2.0	0.5	0.5	0.00

Pos = player position
Total xPt = total expected points for this round, assuming the player plays 96 minutes in each game
Price/xPt = a measure of value: player price per expected point
Min = total minutes played
Shtp96 = shots per 96 minutes
xGp96 = expected goals per 96 minutes
KPp96 = key passes per 96 minutes
xAp96 = expected assists per 96 minutes
RdGamePts = points for playing 60 minutes in each game
BPp96 = bonus points per 96 minutes
xBP = BPp96 * # of games
Pos xCS = expected clean sheet points + expected negative points from goals against

Analysis

Sadly, most of the top players on this list are either out or haven’t been playing the full 90 as of late. Do your research (@mlsinjurynews, etc.) and choose wisely! Here are my top picks who will likely be on the pitch for most of their team’s minutes and are likely to have high floors.

• Ibson: 2.2 BPp96 (bonus points per 96 minutes); played 100% of Minnesota’s minutes for the last 6 games
• Tim Melia: The obvious choice at keeper due to his DGW status and Minnesota’s low xG
• Jimmy Medranda: Who cares if he gets rotated? Even if he only plays 60 minutes in each game, he’s still expected to get 8.6 points, enough to project him higher than all but one NYRB player.
• Hector Villalba: Martinez on international duty, Almiron injured, Minnesota defense with neither Calvo nor Cronin. His 7.7 expected points could probably be higher given that info.

For more fantasy data and insights every round, follow @MLSFantasyStats on Twitter.

Source data behind the calculations come from American Soccer Analysis and MLS Fantasy Cheatsheet.

¹I purposefully don’t project penalties and own goals, since they are nearly impossible to predict due to their infrequency. Expected points from passes, yellow/red cards, fouls received, and saves should be in future models but either require additional data sources (which I don’t have time to tie in yet) or deeper analysis (which I don’t have time to perform yet).