2017 Round 23 Expected Player Offensive Points

Every week, I’ll be publishing a list of top players ranked by expected offensive points. “Offensive points” includes expected shots, goals, assists, and key passes based on the weekly matchup(s) and past performance. It also includes the 2 points the player would receive for playing 60+ minutes. Sadly, offensive bonus points (except key passes) are not included at this time.

Defensive bonus points are also not currently included because of the limitations of the data set.

The basic premise of this model

Take the chance of an event (such as an assist) happening and multiply it by the fantasy value of that event were to occur. This is what is known as the expected value, and it is one of the most common statistical methods used in the fantasy sports community to predict player value, typical or average points on a particular type of play, and player consistency.

If you’d like to read more about expected value and see more examples of its usage, feel free to check out Statistics How To’s explanation.

Please take these assumptions into mind when using this information to select players for your team:

• This model only takes offensive fantasy production into account:  no bonus points or clean sheets are included. There are plenty of good players out there who get bonus points and clean sheets who will not be on this list.
• Player are assumed to play full 96 minutes (the average length of a game) for all games played. If players are to be rotated, especially during double game weeks, their projections will not reflect that knowledge.  If players are substituted in or out for part of the game, such lack of playing time will not be reflected in these projections. The goal of these projections is to say, “If this player plays all 96 minutes, based on his past performance, how many offensive points would he be expected to get?”
• The status of players due to injuries, international call-ups, etc., is unknown to this model. Be sure to check a player’s status before adding him to your team.
• The data set is limited to those players who have played at least 400 minutes, so good players who haven’t played as much this season may not appear on this list.

With that said, let’s get into this round’s data.

Round 23 Expected Player Offensive Points

First	Last	Team	Min	Shtp96	xGp96	KPp96	xAp96	xOffPt
Teal	Bunbury	NE	517	3.71	0.63	1.49	0.17	8.4
Sebastian	Giovinco	TOR	1545	5.78	0.5	1.62	0.15	7.9
David	Villa	NYC	1970	4.82	0.54	2.58	0.29	7.5
C.J.	Sapong	PHI	1959	2.06	0.48	1.08	0.13	7.4
Lee	Nguyen	NE	1929	1.54	0.34	3.33	0.31	7.4
Joao	Plata	RSL	1643	4.03	0.57	2.98	0.29	7.3
Roland	Alberg	PHI	695	2.76	0.51	0.69	0.04	7.2
Erick	Torres	HOU	1576	3.29	0.62	1.52	0.11	7.2
Mauro	Manotas	HOU	1600	3.42	0.54	1.74	0.18	7.2
Jozy	Altidore	TOR	1598	2.76	0.53	1.5	0.16	7.2
Cristian	Colman	DAL	745	3.48	0.52	0.52	0.1	7.0
Bradley	Wright-Phillips	NYRB	1931	3.13	0.5	1.19	0.11	6.8
Patrick	Mullins	DC	438	3.29	0.41	0.66	0.08	6.8
Clint	Dempsey	SEA	1548	4.28	0.59	1.86	0.18	6.7
Alberth	Elis	HOU	1024	3.38	0.49	1.03	0.2	6.7
Kei	Kamara	NE	1593	3.5	0.39	0.9	0.17	6.6
Maximiliano	Urruti	DAL	1759	3.66	0.38	1.04	0.13	6.6
Michael	Barrios	DAL	1580	2.37	0.23	2.19	0.27	6.4
Juan	Agudelo	NE	1164	2.31	0.38	1.4	0.16	6.4
Roland	Lamah	DAL	1449	1.99	0.36	1.59	0.17	6.3
Daniel	Royer	NYRB	1673	2.58	0.44	0.98	0.13	6.3
Romain	Alessandrini	LA	1745	3.58	0.3	2.59	0.29	6.3
Ola	Kamara	CLB	2075	2.82	0.49	0.88	0.14	6.3
Luciano	Acosta	DC	1577	2.07	0.22	2.13	0.18	6.2
Fabian	Herbers	PHI	433	1.77	0.18	1.77	0.24	6.1
Victor	Vazquez	TOR	1611	1.37	0.18	3.4	0.3	6.1
Diego	Fagundez	NE	1833	2.83	0.33	1.2	0.14	6.0
Deshorn	Brown	DC	506	3.04	0.38	0.38	0.02	6.0
Sacha	Kljestan	NYRB	1911	1.61	0.1	3.62	0.33	6.0
David	Accam	CHI	1455	2.71	0.46	2.11	0.28	5.9
Nemanja	Nikolic	CHI	1999	3.75	0.68	0.62	0.06	5.9
Federico	Higuain	CLB	1614	2.38	0.27	2.44	0.22	5.9
Justin	Meram	CLB	1909	2.31	0.31	1.91	0.2	5.8
Romell	Quioto	HOU	752	2.17	0.27	1.28	0.28	5.6
Jose	Guillermo Ortiz	DC	711	2.84	0.27	0.81	0.06	5.6
Jefferson	Savarino	RSL	1034	3.44	0.29	2.14	0.25	5.6
Will	Bruin	SEA	1191	2.98	0.51	1.05	0.1	5.6
Rodney	Wallace	NYC	1369	2.52	0.43	1.33	0.15	5.6
Lamar	Neagle	DC	1215	2.05	0.31	0.79	0.05	5.5
Giovani	dos Santos	LA	1466	2.29	0.35	1.77	0.16	5.5
Patrick	Nyarko	DC	1071	1.61	0.17	1.52	0.18	5.4
Chris	Pontius	PHI	1585	1.88	0.14	1.33	0.2	5.4
Tosaint	Ricketts	TOR	670	2.58	0.38	0.72	0.04	5.4
Yura	Movsisyan	RSL	1355	2.69	0.45	0.85	0.12	5.3
Jay	Simpson	PHI	459	2.09	0.23	0.63	0.1	5.3
Tesho	Akindele	DAL	1075	0.98	0.19	1.96	0.19	5.2
Haris	Medunjanin	PHI	2191	1.05	0.04	2.63	0.23	5.2
Maximiliano	Moralez	NYC	1791	1.5	0.19	2.79	0.3	5.2
Lloyd	Sam	DC	1589	1.15	0.09	2.24	0.19	5.1
Fabrice-Jean	Picault	PHI	1188	2.18	0.23	0.57	0.06	5.0
Albert	Rusnak	RSL	1774	2.65	0.17	3.03	0.22	5.0
Sebastian	Saucedo	RSL	547	3.51	0.23	1.58	0.21	5.0
Alex	Muyl	NYRB	1376	1.67	0.28	0.7	0.13	5.0
	Ilsinho	PHI	1322	2.18	0.15	0.8	0.13	5.0
Niko	Hansen	CLB	448	1.5	0.22	1.07	0.24	5.0

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
xOffPt = total expected offensive points

Analysis

Teal Bunbury has only played two full games, so I don’t expect he’ll meet this lofty projection as the #1 offensive points scorer.  Sebastian Giovinco and David Villa, however, should fill out their expectations; it certainly says something when the third-highest projected offensive player is on an away game.

Same goes for the sixth player in our projections, Joao Plata.  Despite RSL playing away, the D.C. defense gives up a significant amount of offensive production.  Add in Plata’s 1.2 BPp96, and he becomes my sneaky start this week.

Players on this list who have excellent bonus point production and who should be considered higher floor options this week (given they start, of course!):

• Lee Nguyen: 1.5 BPp96 (bonus points per 96 minutes)
• Romain Alessandrini: 1.7 BPp96
• Victor Vazquez: 1.7 BPp96
• Sacha Kljestan: 2.2 BPp96
• Haris Medunjanin:  1.7 BPp96

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

Source data behind the calculations come from American Soccer Analysis.