Ryan Noda — OGILVIE

Ryan Noda1B Norfolk Tides

Age 30 L/L 6'1" / 217 lbs AAA HR +8%BB -5% Svc 3.000 · Ctrl thru 2028

wRC+†95

AVG/OBP/SLG.181 / .318 / .344

PA350

HR12

K%36

BB%14

MLB%10%

Scouting · now → future

HIT

20→20

PWR

45→45

SPD

40→40

EYE

72↘60

trajectory · 10 yrs

0.1peak

2027

Contract

ESTIMATED · service-time + WAR-based estimator (needs verification)

Status Arb-1

MLB Debut 2023

Service 3.000 yrs.days

Team Control Through 2028

First FA Year 2029

Year	Status	Salary	Proj WAR	Value	Surplus
2026	Arb-1	$0.3M	—	—	—
2027	Arb-2	$0.4M	—	—	—
2028	Arb-3	$0.7M	—	—	—
Totals		$-6M		$-6M	+$0.0M

Career Projections (MLE)

All seasons translated to park-neutral MLB-equivalent (MLE) and combined per year. 2026 = actual YTD + projected ROS + total. 2027+ via Advanced Marcel + aging curve.

Year	Age	Lvl	Team	PA	AB	H	2B	3B	HR	BB	SO	HBP	SB	CS	AVG	OBP	SLG	OPS	ISO	BABIP	K%	BB%	wOBA	wRC+	BAT	RUN	FLD	POS	REP	WAR
2017	21	ROK	Bluefield Blue Jays	276	238	66	14	2	6	35	85	3	7	4	.277	.377	.429	.805	.151	.408	31	13	.356	131	+10	-0	+0	-5	+8	1.2
2018	22	A	Lansing Lugnuts	527	436	87	19	3	16	76	173	10	14	4	.200	.331	.367	.698	.167	.282	33	14	.308	94	-4	+1	+0	-9	+15	0.3
2019	23	A+	Dunedin Blue Jays	469	399	64	21	0	8	53	204	10	14	2	.160	.275	.273	.548	.113	.290	43	11	.254	53	-25	+2	+0	-8	+13	-1.9
2021	25	AA	Tulsa Drillers	475	410	81	11	1	27	48	171	12	3	1	.198	.300	.427	.727	.229	.249	36	10	.316	100	+0	+0	+0	-8	+13	0.5
2022	26	AAA	Oklahoma City Dodgers	574	497	99	19	1	19	59	207	15	20	4	.199	.303	.356	.659	.157	.292	36	10	.293	82	-11	+2	+0	-10	+16	-0.3
2023	27	MLB	2 teams	513	419	97	21	1	18	82	174	10	3	1	.232	.370	.415	.785	.184	.345	34	16	.352	127	+16	+0	-6	-9	+15	1.5
2024	28	AAA	2 teams	558	466	74	19	1	17	80	206	9	5	1	.159	.294	.313	.607	.155	.233	37	14	.279	72	-18	+1	-0	-10	+16	-1.2
2025	29	AAA	2 teams	350	279	42	5	0	10	53	141	14	7	1	.151	.315	.276	.591	.125	.244	40	15	.276	70	-12	+1	-0	-6	+10	-0.8
▸ 2026 Season (actual · ROS · total)
2026	30	AAA	Norfolk Tides	40	34	8	1	0	2	2	19	3	0	0	.235	.333	.441	.775	.206	.462	48	5	.304	91	-0	+0	+0	-1	+1	0.0
ROS	30	MLB	BAL	310	256	48	9	1	11	42	115	9	3	1	.188	.322	.359	.682	.172	.278	37	14	.310	95	-2	+0	-3	-6	+9	-0.1
TOT	30	MLB	BAL	350	290	56	10	1	13	44	134	12	3	1	.193	.324	.369	.693	.176	.297	38	13	.309	95	-2	+0	-3	-6	+10	-0.1
▸ 2027+ Projections — Projections at projected PA · Park-neutral MLB equivalent · Marcel + aging curve
2027	31	MLB 20%	BAL	445	368	69	13	1	15	62	148	12	3	1	.188	.324	.351	.674	.163	.257	33	14	.309	95	-3	+0	-7	-8	+13	-0.5
2028	32	MLB 33%	BAL	426	352	65	12	1	14	59	144	12	1	0	.185	.322	.344	.665	.159	.255	34	14	.305	91	-4	+0	-8	-8	+12	-0.7
2029	33	MLB 43%	BAL	396	328	60	11	1	13	54	137	11	0	0	.183	.318	.341	.660	.159	.253	35	14	.299	87	-6	+0	-8	-7	+11	-1.0
2030	34	MLB 52%	BAL	351	291	50	9	0	11	48	124	9	0	0	.172	.307	.316	.624	.144	.250	35	14	.293	82	-7	+0	-8	-6	+10	-1.2
▸ Career Totals (MLE all levels + projections)
Career	—			5670	4739	902	183	13	185	751	2029	136	80	20	.190	.318	.352	.670	.161	.280	36	13	.303	90	-66	+8	-41	-101	+161	-4.0
/ 162G	—			650	543	103	21	1	21	86	233	16	9	2	.190	.318	.348	.666	.158	.280	36	13	.303	90	-8	+1	-5	-12	+18	-0.5

Historical Stats — Raw (Per Stint)

Actual MLB + MiLB stats as they happened, one row per stint at each level. Use this to see what really got recorded; the table above shows MLB-equivalent translations + projections.

Year	Age	Lvl	Team	PA	AB	H	2B	3B	HR	BB	SO	HBP	SB	CS	AVG	OBP	SLG	OPS	ISO	BABIP	K%	BB%	wOBA	wRC+	BAT	RUN	FLD	POS	REP	WAR
2017	21	ROK	Bluefield Blue Jays	276	214	78	18	3	7	59	60	3	7	4	.364	.507	.575	1.082	.210	.483	22	21	.475	222	+38	-0	—	—	+8	4.7
2018	22	A	Lansing Lugnuts	527	403	103	24	4	20	109	135	10	14	4	.256	.425	.484	.909	.228	.328	26	21	.402	166	+39	+1	—	—	+15	5.7
2019	23	A+	Dunedin Blue Jays	469	378	90	27	1	13	74	138	10	14	2	.238	.377	.418	.795	.180	.330	29	16	.355	130	+16	+2	—	—	+13	3.2
2021	25	AA	Tulsa Drillers	475	384	96	15	1	29	74	127	12	3	1	.250	.387	.521	.908	.271	.288	27	16	.395	161	+33	+0	—	—	+13	4.7
2022	26	AAA	Oklahoma City Dodgers	574	464	120	23	1	25	92	162	15	20	4	.259	.398	.474	.872	.216	.339	28	16	.388	155	+36	+2	—	—	+16	5.5
2023	27	MLB	Oakland Athletics	495	406	93	22	1	16	77	170	10	3	1	.229	.365	.406	.772	.177	.347	34	16	.350	126	+14	+0	-6	-9	+14	1.3
2023	27	AAA	Las Vegas Aviators	18	10	4	2	0	2	8	3	0	0	0	.400	.667	1.200	1.867	.800	.400	17	44	.684	381	+6	+0	—	—	+1	0.6
2024	28	MLB	Oakland Athletics	111	95	13	4	0	1	14	37	1	0	0	.137	.255	.211	.465	.074	.211	33	13	.225	30	-9	+0	-0	-10	+3	-1.6
2024	28	AAA	Las Vegas Aviators	447	348	78	21	2	22	89	134	8	5	1	.224	.393	.486	.879	.261	.289	30	20	.389	156	+28	+1	—	—	+13	4.2
2025	29	MLB	Baltimore Orioles	59	47	5	0	0	1	11	25	0	1	0	.106	.276	.170	.446	.064	.190	42	19	.227	32	-5	+0	-0	-6	+2	-1.0
2025	29	AAA	Norfolk Tides	291	208	39	6	0	10	66	94	14	6	1	.188	.413	.361	.774	.173	.271	32	23	.363	136	+12	+1	—	—	+8	2.1
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			665	548	111	26	1	18	102	232	11	4	1	.203	.339	.352	.691	.150	.310	35	15	.318	101	+1	+0	-7	-25	+19	-1.3

Career Path Comps

What happened to players with a similar stat profile at this age · Y-axis: wRC+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

		Scouting				At Match Age									Next 3 Yrs									Peak 3 Yrs
Comp	Age	HIT	PWR	SPD	EYE	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR
Ryan Noda (OGILVIE)	30	20	45	40	60	.181/.318/.344	.164	.266	12	4	36%	14%	93	+0.7	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Yoshi Tsutsugo 2022 · MLB	30	42→41	54→50	42→30	53	.224/.312/.404	.179	.271	22	3	26%	11%	103	+1.9	.221/.310/.369	.148	.294	18	4	29%	11%	93	+1.3	.209/.307/.339	.130	.290	16	2	31%	12%	81	+0.2
✕Jon Singleton 2023 · AAA	31	38→48	53→55	47→45	53	.200/.302/.371	.171	.275	21	5	34%	13%	91	+1.1	.210/.302/.378	.168	.272	22	3	29%	12%	92	+1.2	.198/.292/.355	.157	.252	21	1	29%	12%	79	+0.2
✕Jabari Blash 2018 · MLB	29	41→40	55→60	43→50	49	.210/.315/.398	.188	.303	24	6	36%	12%	103	+1.9	.222/.315/.429	.207	.328	27	9	38%	10%	111	+2.5	.210/.301/.454	.245	.297	34	10	40%	9%	108	+1.5
✕Greg Bird 2022 · MLB	29	38→46	53→55	43→30	45	.206/.279/.372	.166	.266	22	4	31%	8%	81	+0.4	.206/.274/.347	.141	.276	17	6	30%	8%	72	-0.2	.179/.252/.288	.109	.243	13	4	30%	8%	44	-0.9
✕Daniel Palka 2022 · MLB	30	40→43	53→60	49→50	48	.215/.286/.385	.171	.264	22	6	28%	9%	87	+0.8	.225/.283/.396	.171	.284	23	5	29%	7%	89	+0.9	.215/.272/.382	.166	.269	24	2	29%	7%	75	+0.0
✕Kirk Nieuwenhuis 2018 · MLB	30	42→50	49→55	44→50	49	.209/.300/.358	.149	.303	16	11	34%	11%	86	+0.8	.203/.285/.319	.116	.322	10	10	36%	10%	69	-0.4	.182/.265/.284	.103	.302	6	8	38%	9%	50	-0.9
✕Roman Pena 2016 · AAA	29	37→47	50	43→45	41	.192/.265/.339	.147	.299	18	7	40%	9%	66	-0.6	.201/.255/.324	.123	.332	13	10	41%	6%	57	-1.2	.232/.266/.344	.113	.357	7	16	36%	4%	65	-0.1
✕Jeff Larish 2012 · MLB	29	43→45	53→55	42→45	45	.225/.294/.396	.171	.296	21	4	31%	8%	93	+1.3	.201/.271/.337	.136	.296	14	5	34%	9%	68	-0.5	.157/.239/.251	.094	.251	4	0	36%	10%	30	-1.1
✕Jarrett Parker 2019 · MLB	30	45→50	52→55	45→50	45	.228/.302/.393	.165	.309	20	6	32%	9%	95	+1.4	.212/.300/.380	.168	.314	21	6	36%	11%	92	+1.2	.182/.286/.352	.170	.266	23	2	38%	12%	79	+0.2
✕Andy Wilkins 2019 · MLB	30	34→44	54→55	51→45	42	.192/.258/.362	.170	.249	23	6	33%	8%	69	-0.4	.192/.261/.346	.154	.301	20	6	41%	8%	66	-0.6	.130/.214/.248	.118	.223	17	0	48%	9%	22	-1.3
✕Ryan Langerhans 2012 · MLB	32	44→41	51→50	45→50	52	.222/.314/.382	.160	.300	19	13	31%	11%	98	+1.6	.207/.296/.357	.150	.303	17	9	34%	11%	84	+0.6	.186/.278/.331	.145	.274	16	5	35%	11%	67	-0.3
✕Joe Koshansky 2011 · MLB	29	37→46	54→60	44→40	39	.199/.268/.373	.174	.295	23	5	39%	8%	77	+0.1	.202/.268/.343	.141	.307	17	8	38%	7%	69	-0.4	.152/.220/.250	.098	.240	10	6	39%	7%	21	-0.9
✕Matt Skole 2018 · AAA	29	37→39	51→50	46→40	44	.201/.264/.359	.158	.247	20	3	28%	7%	71	-0.3	.199/.271/.339	.140	.267	16	4	31%	8%	69	-0.4	.188/.269/.317	.129	.256	16	2	31%	9%	61	-0.8
✕Matt Davidson 2021 · MLB	30	39→44	56→60	46→30	42	.214/.285/.399	.186	.287	25	3	34%	8%	91	+1.1	.228/.297/.443	.215	.296	31	3	33%	7%	108	+2.3	.223/.299/.448	.226	.282	34	0	34%	7%	106	+1.4
✕Chris Parmelee 2019 · AAA	31	39→49	49→50	44→45	46	.207/.273/.352	.145	.263	18	5	28%	8%	72	-0.2	.212/.286/.385	.173	.306	23	5	36%	9%	87	+0.8	.183/.269/.366	.183	.250	27	0	37%	10%	75	-0.0
✕Baltazar Lopez 2014 · AAA	30	46	50	41→45	38	.231/.279/.383	.152	.320	18	4	33%	6%	82	+0.5	.226/.279/.354	.128	.332	14	6	34%	6%	76	+0.0	.214/.285/.264	.050	.374	4	2	40%	7%	56	-0.3
✕Graham Koonce 2006 · AAA	31	43→41	54→55	43→30	45	.229/.291/.403	.173	.279	23	4	27%	7%	94	+1.3	.214/.287/.393	.179	.267	23	4	28%	8%	89	+1.0	.191/.275/.374	.183	.232	24	2	29%	9%	78	+0.1
✕Ryan Schimpf 2018 · MLB	30	32→39	61→60	40→45	48	.195/.286/.416	.221	.240	30	3	33%	10%	96	+1.5	.189/.273/.364	.175	.295	22	6	41%	9%	76	+0.1	.114/.195/.228	.115	.222	10	4	50%	8%	7	-1.0
✕Paul McAnulty 2011 · MLB	30	43→49	53→55	39→40	48	.226/.294/.395	.169	.266	21	2	24%	8%	92	+1.2	.230/.291/.401	.171	.276	21	4	24%	7%	93	+1.3	.223/.290/.397	.174	.268	21	3	25%	8%	90	+0.9
✕Cody Decker 2016 · AAA	29	36→39	54→65	41→30	40	.197/.260/.372	.175	.271	23	2	36%	7%	73	-0.1	.192/.249/.352	.160	.286	21	3	39%	6%	63	-0.8	.183/.255/.341	.158	.267	21	0	38%	8%	62	-0.3

Contract

Career Projections (MLE) Show advanced cols

Historical Stats — Raw (Per Stint)

Career Path Comps

Career Projections (MLE)